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# A systematic mapping study on software-based feedback for energy consumption

Thomas Zaragoza, Adel Noureddine, Ernesto Exposito

► To cite this version:

Thomas Zaragoza, Adel Noureddine, Ernesto Exposito. A systematic mapping study on software-based feedback for energy consumption. Renewable and Sustainable Energy Reviews, 2025, 222, pp.115889. 10.1016/j.rser.2025.115889. hal-05107298

HAL Id: hal-05107298

https://hal.science/hal-05107298v1

Submitted on 11 Jun 2025

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# Highlights

## A Systematic Mapping Study on Software-Based Feedback for Energy Consumption

Thomas Zaragoza, Adel Noureddine, Ernesto Exposito

* Among the energy feedback systems examined in this study, the predominant design emphasis was on the building sector, where software engineering took center stage as the driving force for sustainability, as opposed to the target for sustainability.

* We observed that a significant portion of publications implemented and tested their contributions, but a relatively small subset conducted field experiments, with even fewer incorporating control groups and/or long-term post-intervention monitoring to assess the sustainability of energy reductions. Additionally, our analysis revealed that there were limited publications that employed surveys in the form of questionnaires, focus groups, or interviews to gather insights from human participants.

* When ICT was the target of sustainability, we found a tendency to focus on providing energy feedback to experts (developers and researchers) rather than end users which we believe outlines room for progress to emulate the awareness in the building sector.
# A Systematic Mapping Study on Software-Based Feedback for Energy Consumption

Thomas Zaragoza<sup>a,*</sup>, Adel Noureddine<sup>a</sup> and Ernesto Exposito<sup>a</sup>

<sup>a</sup>Université de Pau et des Pays de l'Adour, E2S UPPA, LIUPPA, Pau, 64000, France

## ARTICLE INFO

**Keywords:**

Surveys and overviews
Feedback
Green IT
Energy

## ABSTRACT

In the context of the rising energy consumption of ICT and its environmental impacts, there is a need to raise user awareness on the energy impact of their ICT devices and software. We mapped the existing literature around this topic by conducting a systematic mapping study on software approaches for providing energy feedback. We have found that while providing energy feedback is common in the building sector under the guise of smart-home and smart grid applications, the ICT sector is sparsely monitored. When ICT is the patient of sustainability (Green IT / Green in IT) rather than the engine (IT for Green / Green by IT), we found that energy monitoring software are predominantly aimed towards experts (developers and researchers) and decision makers rather than end users. By contrast, when ICT is the driving force for sustainability, energy monitoring software are predominantly aimed towards end users. We believe this contrast in targeted actors shows the need for more publications in Green IT (Green in IT) for raising awareness and reducing software energy consumption in end users through the use of persuasive technology and energy feedback systems already in-use and tested in the building sector.

## CRediT authorship contribution statement

**Thomas Zaragoza:** Writing — original draft and Investigation. **Adel Noureddine:** Writing — review & editing, Funding acquisition and Project administration. **Ernesto Exposito:** Writing — review & editing.

## 1. Introduction & Context

The urgent need to reduce greenhouse gas emissions (GHGE) and resource consumption has made sustainability a global priority. Within this context, Information and Communication Technology (ICT) plays a complex dual role—it serves as both a tool for sustainability and a growing contributor to energy consumption. On one hand, ICT supports sustainability efforts in various sectors through IT for Green (Green by IT) applications, such as smart grids for optimized energy distribution, traffic management systems for reducing congestion, and digital platforms that enhance resource efficiency. On the other hand, the increasing reliance on software applications and digital services has led to a steady rise in energy demand, contributing significantly to GHGE. The rapid growth of energy-intensive technologies, such as artificial intelligence (AI), further exacerbates this issue, making it increasingly difficult to overlook ICT's environmental footprint. The ICT sector, in 2020, accounted for 4–6% of global electricity consumption and 1.4% of greenhouse gas emissions (GHGE), with user devices and data centers being the largest contributors [1]. This marks a significant increase from the early 2010s, when ICT represented just 1–2% of global electricity use [2]. The sector's growth is driven by surging data traffic (40x since 2007) and energy-intensive 5G networks, which consume three times more power per base station than 4G. Despite efficiency gains, absolute emissions have risen due to infrastructure expansion, with data centers alone projected to double electricity demand by 2026 [3] [4]. The AI boom is exacerbating these trends. Training large models like GPT-3 emits 552 metric tons of CO2—equivalent to a coal plant running for 10 hours—while AI inference workloads could add 1–3% to global electricity demand by 2030 [3] [5]. Google reported a 13% rise in emissions in 2024 due to AI-driven data center growth, despite using 90% carbon-free energy [6]. Hyperscale AI data centers demand 10–50x more power per rack than traditional servers, with water use for cooling rising by 20–34% annually [7]. While AI can optimize energy use (e.g., reducing building emissions by 8–19%), its

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<footnote>*Corresponding author</footnote>

<footnote>✉ thomas.zaragoza@univ-pau.fr (T. Zaragoza); adel.noureddine@univ-pau.fr (A. Noureddine); ernesto.exposito@univ-pau.fr (E. Exposito)
ORCID(s): https://orcid.org/0000-0002-0093-5901 (T. Zaragoza); 0000-0002-8585-574X (A. Noureddine); https://orcid.org/0000-0002-3543-2909 (E. Exposito)</footnote>
net impact risks outpacing renewable's deployment, demanding stricter efficiency standards and grid de-carbonization to align with climate goals [5, 6].

Traditionally, researchers have focused on hardware and software optimizations to mitigate ICT's carbon footprint. Hardware improvements, such as more energy-efficient CPUs, storage devices, and cooling systems, have been widely explored [8, 9]. Additionally, virtualization and cloud computing have enabled better resource allocation, while algorithmic optimizations have enhanced data center efficiency [10, 11, 12]. Despite these advancements, the carbon footprint of ICT continues to grow, suggesting that technical optimizations alone are insufficient. A key missing factor in energy consumption discussions is the role of the user. Human behavior plays a critical role in energy consumption, yet it is often overlooked in ICT energy reduction strategies. One significant behavioral phenomenon is the rebound effect, where efficiency improvements unexpectedly lead to higher consumption. For example, Jevons' Paradox illustrates how advancements in steam engine technology, which made coal usage more efficient, paradoxically led to an increase in coal consumption [13]. A similar pattern can be observed in ICT: as software and hardware become more efficient, users may increase their usage, offsetting potential energy savings.

Given the significant role of user behavior, addressing ICT's sustainability challenge requires solutions that go beyond purely technical optimizations. Research on environmentally responsible behavior highlights intrinsic satisfaction—such as a sense of accomplishment in resource management—as a key motivator for sustainable actions. Energy feedback mechanisms can leverage this motivation by providing users with real-time or periodic insights into their energy consumption, enabling more informed and efficient usage decisions.

Energy feedback has been widely studied in residential and commercial sectors, such as smart homes and energy management systems. However, applying these mechanisms within ICT presents unique challenges, including the need to adapt feedback models to software usage, develop effective interventions, and mitigate potential rebound effects. Understanding how feedback influences user behavior in the ICT context is essential for designing solutions that encourage more sustainable software use.

To provide a comprehensive overview of energy feedback in ICT, this study conducts a systematic mapping survey of existing research on energy-related feedback mechanisms. Specifically, we aim to: (1) identify core features and mechanisms used in energy feedback solutions, (2) analyze gaps in current research on feedback-driven behavioral change, and (3) explore how existing approaches can be adapted to reduce software energy consumption.

The following sections outline our methodology, research questions, and findings, offering insights into how feedback can serve as a tool for promoting sustainable software use.

## 2. Related works

Several literature reviews, mapping studies, and surveys have examined sustainability and energy feedback in the context of software and ICT. However, our systematic mapping study (SMS) distinguishes itself by focusing specifically on software's dual role: as a medium for delivering energy-related feedback (IT for Green / Green by IT) and as an object of energy optimization (Green IT / Green in IT).

Most existing studies emphasize energy feedback in domains such as smart homes, smart grids, and industrial systems, primarily from the IT for Green perspective [14, 15]. While these offer valuable insights into how ICT facilitates sustainability, they often neglect the energy footprint of ICT systems themselves. Our SMS fills this gap by analyzing software-based approaches to energy efficiency, from code-level optimizations to feedback mechanisms that influence end-user behavior.

Research in this area also varies by target audience. Some studies focus on tools for experts—developers and operations teams—that prioritize technical aspects like system monitoring and log data analysis [16]. Others aim at end users, employing visualization techniques to encourage energy-conscious behavior [17]. However, few reviews integrate both perspectives. Our SMS bridges this divide by examining both expert- and user-facing feedback systems.

In addition, literature on feedback mechanisms often isolates visualization strategies or gamification elements. Some studies explore how visualizations impact user engagement and behavior [17], while others examine the persuasive power of gamified features [18]. Our SMS instead considers the interaction between different feedback types, providing a more holistic view of how these elements jointly influence sustainability outcomes.

Prior mapping studies in green software engineering have highlighted notable limitations, such as limited empirical validation and an overemphasis on technical contributions. For instance, one review points to the underuse of software analytics in energy-aware development [19], while another identifies a focus on sustainability frameworks with minimal attention to end-user interaction or real-time feedback [20]. Broader classifications of technical and non-technical
approaches often overlook behavior-shaping feedback features [21], and early foundational work identified clusters of research activity without extensive real-world uptake [22]. Our study complements this body of work by narrowing in on software-mediated feedback systems, a recurring yet previously underexplored element in Green IT, to assess their design, application context, and role in shaping user behavior.

Finally, although many studies discuss feedback effectiveness, few include systematic evaluation of testing methodologies. Particularly in the ICT domain, empirical validation with human participants remains rare. Our SMS addresses this gap by assessing the robustness of evaluation methods across studies, revealing a need for more rigorous, evidence-based validation of feedback systems in sustainability-oriented software engineering.

## 3. Study Methodology

<figure><img src="image_4.png" /><figcaption>Figure 1: Flowchart of the search process</figcaption></figure>

We have conducted this systematic mapping study according to the guidelines of Petersen et al. [23] [24] for conducting this type of studies in software engineering which can be summarized as :

1. The definition of research questions to scope the study and provide an overview of trends and results available within it.

2. The search of primary studies on scientific databases using relevant search strings and/or the manual browsing of pertinent journals and/or conference proceedings.

3. Screening of papers and the application of inclusion and exclusion criteria.

4. Keywording of abstractions and classification of papers.

5. Extraction of data, mapping of studies and analysis.

Adjustments were made according to the needs of our study and the methodology can be visualized in the flowchart on Figure 1. Notably we have added the *Term Frequency - Inverse Document Frequency* (TF-IDF) statistical method into the process to refine our initial query and we did not perform the keywording of abstractions which Peterson describes as a process to facilitate the creation of a classification scheme that considers existing studies.

TF-IDF is a numerical statistic method used in information retrieval and text mining to measure the importance of a term within a document or a corpus (a collection of documents). TF-IDF is based on the intuition that terms that appear frequently in a document, but rarely in the entire corpus, are more likely to carry significant meaning or be indicative of the document's content.

We conducted an initial iteration using the original query. Afterward, we applied a three-step filtering process: (1) removing duplicates resulting from overlapping academic sources, (2) excluding papers whose titles or abstracts did not meet the inclusion criteria, and (3) discarding papers that initially passed but, upon deeper reading, were found not to satisfy the inclusion/exclusion criteria. We then used the titles and abstracts of the remaining papers to perform a TF-IDF analysis, refining our keywords iteratively until no new relevant terms emerged. This process is detailed further in the corresponding subsection.

### 3.1. Research Questions

As the aim of this paper is to study the link between energy consumption feedback and the role software plays as both an agent and patient of sustainability we seek to answer the following questions:
**RQ1: How has the research on energy feedback in software systems evolved over time, and what does it reveal about software's dual role in sustainability—both as a driver and a target?**

The aim of this research question is to study the trends of software-based energy feedback and the role of ICT as both a driving force or a patient for sustainability. We are not restricting the application of this study uniquely to the ICT sector. Previously we have seen how ICT has been utilized to promote sustainability in various domains, with an emphasis on its role as a driving force rather than a patient. By analyzing current software approaches that provide energy feedback, we can confirm this trend while also gaining insights into the maturity level and interest in this field.

**RQ2: What are the different forms and variations of feedback? To what extent are these approaches tested?**

While, by design of the systematic mapping study, all the publications in this study provide energy feedback to a third party, the method by which they do so may differ in a multitude of ways. The aim of this research question is to propose a feedback classification scheme to compare feedback systems between publications and to study how thoroughly tested these designs are according to the field of application and targeted actors.

**RQ3: What are the existing software approaches for providing feedback to users on their software energy consumption?**

Unlike in the previous question, here we are limiting ourselves to the ICT field of application as to focus on ICT's patient relation with sustainability and how feedback can achieve sustainability in ICT on a hardware or software level. We consider optimizations to be made on a software level when source code is optimized or the end user's software usage behavior is altered to achieve reductions while we consider hardware optimizations to be reductions achieved on the system as a whole through optimized components on overall reduced usage. For example, we would consider a user optimizing the options on a software or choosing an energy efficient alternative as a software optimization while considering automatically turning off a monitor when a user steps away as a hardware optimization.

TITLE-ABS-KEY((Household OR User OR Consumer OR Human OR Personal OR Enduser OR Individual) AND (Feedback OR Visual*) AND (Energy OR Power OR Green) AND (Software)) AND (LIMIT-TO (SUBJAREA, "COMP" ) ) AND (LIMIT-TO (LANGUAGE, "English"))

<caption>**Figure 2:** Initial scopus search query</caption>

As recommended by Petersen et al. in their updated guideline for conducting a systematic mapping study in software engineering [24], we used the PICO (Population, Intervention, Comparison, and Outcomes) framework to systematically define our research scope and derive a search string that aligns with our study's focus.

* Population (P): We identified the population as "software engineering studies discussing energy feedback", ensuring that our search targets research using software systems to provide feedback.

* Intervention (I): The intervention of interest is "energy consumption", meaning we focus on studies that investigate how energy feedback is provided rather than general discussions on sustainability or efficiency.

* Comparison (C): Since our study is a systematic mapping rather than an experimental comparison, we did not enforce a strict comparative element but ensured our selection covered multiple feedback approaches.

* Outcomes (O): The expected outcome is a **structured overview of software-based energy feedback mechanisms**, identifying key approaches, gaps, and potential applications within ICT sustainability.

Based on these definitions, we formulated our search string iteratively, refining it to maximize relevance while minimizing noise. The core terms—"feedback," "software," and "energy"—were directly derived from our research questions. To ensure comprehensive coverage, we expanded the feedback clause by including variations such as visual, capturing studies that describe energy-related data visualization as a feedback mechanism. Similarly, we enriched the energy clause with commonly used alternatives such as power and green. The recipient clause posed the greatest challenge, as feedback is directed at human users, yet terminology varies across disciplines. To address this, we compiled a diverse set of recipient terms reflecting different contexts:
* "Consumer" for product feedback systems,

* "Household" for smart-home applications,

* "User" for software-related feedback.

We further included synonyms like "end-user," "home," "human," "individual," and "personal", ensuring we captured all relevant studies discussing energy feedback directed at human decision-makers. By employing **PICO systematically** and refining our search terms iteratively, we ensured that our query effectively targeted **software-based energy consumption feedback studies** across multiple domains without introducing excessive unrelated results. The final search string used for the SCOPUS database is detailed in **Figure 2**.

We then translated and adapted this query to fit the syntax and limitations of all five publication databases queried. Neither *IEEE*, *ACM DL*, nor *Web of science* had a field search that allowed us to search for entries that contained our search strings simultaneously in either their titles, abstracts or keywords thus requiring us to split the query for each field. Additionally we were limited by *Science direct* which wouldn't allow us to use more than eight Boolean connectors which we circumvented by unifying the results of multiple sub-requests.

The inclusion criteria were elaborated to reflect our research questions.

**IC1: The publication's main contribution is software related.**

While we do not limit the scope of this study to the software field of application, we do limit the scope to the software engineering field. The main contribution should be the proposition or development of a software architecture and/or artifact.

**IC2: The publication's main contribution is providing energy feedback to a third party.**

The aim of the contribution should be to provide energy feedback to a third party. Our scope of third-party involvement is not restricted solely to human actors, but also encompasses publications engaged in delivering feedback within an autonomous pipeline. Through this we believe we can include works that, while we do not explicitly mention humans in the equation, still offer a form of energy feedback like non-intrusive load monitoring.

**IC3: The publication is peer reviewed.**

The paper should be published at a conference or in a journal requiring a jury of expert peer reviews to assure the validity of the method employed and the results obtained.

A publication was excluded if it fulfilled one of the following criteria:

**EC1: The publication is not in English.**

We limited the scope of our study to English which is broadly used and understood in the scientific community and members of our team. While we can limit the result of publications to English in the following databases: *Web of Science*, *Scopus* and *Science Direct* we had to manually exclude non-English publications originating from *ACM Digital Library* and *IEEE Xplore*.

**EC2: The publication is superseded.**

We choose to exclude superseded publications by the same primary authors of the same project. In the case two publications have the same authors and present the same contributions and validate the inclusion criteria, we include only the most recent publication.

**EC3: The publication is not accessible publicly or through our academic resources.**

In the case where a publication satisfied the inclusion criteria but could not be accessed publicly or through our resources, we contacted the authors directly to request their paper. In the case where we did not receive a response, we unfortunately had to exclude the paper.
<table><caption>**Table 1**
Initial distribution of publications found per source</caption><thead><tr><td>Source</td><td>#Publications</td></tr></thead><tbody><tr><td>IEEE</td><td>920</td></tr><tr><td>ACM DL</td><td>2651</td></tr><tr><td>Science Direct</td><td>393</td></tr><tr><td>Scopus</td><td>1785</td></tr><tr><td>Web of Science</td><td>566</td></tr><tr><td>Total</td><td>6089</td></tr></tbody></table>

<table><caption>**Table 2**
Top five most frequent terms</caption><thead><tr><td>Term</td><td>Frequency</td></tr></thead><tbody><tr><td>Power</td><td>0.030</td></tr><tr><td>Monitor*</td><td>0.030</td></tr><tr><td>Feedback</td><td>0.029</td></tr><tr><td>Electr*</td><td>0.027</td></tr><tr><td>Build*</td><td>0.025</td></tr></tbody></table>

<table><caption>**Table 3**
Final distribution of papers found per source</caption><thead><tr><td>Source</td><td>#Publications</td></tr></thead><tbody><tr><td>IEEE</td><td>945</td></tr><tr><td>ACM DL</td><td>3924</td></tr><tr><td>Science Direct</td><td>523</td></tr><tr><td>Scopus</td><td>5400</td></tr><tr><td>Web of Science</td><td>1668</td></tr><tr><td>Total</td><td>12460</td></tr></tbody></table>

## 3.2. Search Process

On our initial query in March of 2023, after pruning duplicates we found both due to a quirk of ACM's digital library and the union of results from sub-queries, we obtained the results visualized on Table 1. We filtered publications according to title and abstracts and our exclusion and inclusion criteria until there remained only **39 publications**.<fnref n="1" /> Following deeper analysis, these papers were excluded from the final corpus. We then performed a term frequency-inverse document frequency analysis of titles, abstracts and keywords after deletion of *stop words* (which is a term used in *natural language processing* (NLP) to refer to words that are commonly found in a language but typically do not contribute much meaning to the overall understanding of a text e.g. 'an', 'the', etc.) and *stemming* (also a term used in NLP that refers to the reduction of words to their base or root form, known as the stem e.g. 'running' -> 'run'). We can see an example of the five most frequent terms represented in Table 2.

TITLE-ABS-KEY((Household OR User OR Consumer OR Human OR Personal OR Enduser OR Individual OR Build* OR Home OR Resident*) AND (Feedback OR Visual* OR Monitor* OR Meter*) AND (Energy OR Power OR Green) AND (Software) AND (LIMIT-TO ( SUBJAREA, "COMP" ) ) AND (LIMIT-TO ( LANGUAGE, "English"))

<caption>**Figure 3:** Final scopus search query</caption>

<footnote n="1">¹It is important to note that 10 papers, despite meeting the title and abstract inclusion criteria, could not be sourced. We attempted to obtain these publications by reaching out directly to the authors via email, *LinkedIn*, and *ResearchGate*. None responded</footnote>
After analyzing the most frequent terms and deliberating on their relevance, we decided to expand the *user* clause to include inanimate third parties using the terms *build*\*, *home*, and *resident*\*. Additionally, we broadened the *feedback* clause to incorporate the terms *monitor*\* and *meter*\*. The term *meter*\* was included because it captures essential energy monitoring tools critical to feedback mechanisms. Keywords such as *meter*, *metering*, and *smart meter* are directly related to measuring energy consumption—an integral aspect of actionable feedback. As our study focuses specifically on software-based energy feedback, incorporating metering terms helps ensure we capture relevant literature. Conversely, the term *electr*\* was excluded due to its excessive generality, which led to the inclusion of numerous irrelevant results. While electricity is fundamental to the domain, the term *electr*\* frequently appears in unrelated contexts such as electrical engineering or power infrastructure, thereby introducing noise. By including *meter*\* and omitting *electr*\*, we refined the search to remain aligned with our focus on energy feedback research. The total number of publications retrieved per source prior to filtering is presented in Table 3. We removed previously analyzed publications, applied the same title/abstract screening and TF-IDF analysis, and determined that no further expansion of our search clauses was necessary.

Using the updated query, we conducted an additional search which yielded **51 new publications**.²

To ensure the study remained current, two additional extractions took place in 04/2024 and 05/2025 using the query shown in Figure 3. This final update included results from the initial search as well as newly published papers. As a result, no publications dated beyond 2024, are included. This final search added **3 additional papers**. It is worth noting that several relevant studies identified during earlier screenings could not yet be accessed, either due to their recent publication dates or limited availability. This limitation likely contributed to the apparent decline in included publications from 2023 and 2024.

From the remaining 93 publications, we have snowballed backwards as recommended in the publication of *Samireh Jalali and Claes Wohlin* [25] resulting in the addition of 143 publications for a total of 235 publications. We did not perform forward snowballing due to its practical limitations. Unlike backward snowballing, which relies on clearly listed references, forward snowballing depends on citation tracking features that are inconsistently supported across databases, making the process less systematic and harder to reproduce. Moreover, forward snowballing tends to introduce a high volume of noise, often surfacing marginally related or tangential papers, and can result in redundant results already captured through backward snowballing. With the research questions elaborated, the publications chosen and the data extracted, we can begin the analysis of the aforementioned data to provide an answer to our questions.

## 3.3. Data Extraction & Snowballing

For data extraction we used the template provided by Petersen et al. [24] and represent the data items and values we wish to extract in Table 4 with their corresponding research questions.

Venue types, names, publishing dates and author keywords could generally be extracted automatically with the latter occasionally be unavailable or manually extracted. Publication venue types were regrouped into two distinct classes: Journal or Proceedings with journal regrouping articles published directly into scientific journals or magazines and proceedings regrouping conference proceedings and workshops.

Feedback types can take two forms: direct and/or indirect. The distinction lies in the relationship between the feedback system and the recipient. In the case of direct feedback, the user actively seeks it out, while in the case of indirect feedback, the system interrupts the user to deliver it. Feedback frequency ranges from immediate real-time feedback to long-term. Additionally we consider reports as one-time audits of software, consumer goods, or residential consumption. We split feedback methods into the following categories :

1. **Injunctive feedback** communicates norms, rules, or expectations to guide future behavior. Unlike descriptive feedback, it includes recommendations or directives, aiming to shape actions, ensure compliance, and promote positive change—e.g., a traffic light where green means go and red means stop.

<footnote n="22">²²8 of the initially retained papers could not be retrieved in full text. We reached out to the corresponding authors via email, LinkedIn, and ResearchGate. Out of 19 contact attempts, only 4 responded. After further review, these inaccessible papers were excluded from the final corpus.</footnote>
<table><caption>**Table 4**

Data extraction form used in this study, fields labeled with an asterisk were extracted automatically</caption><thead><tr><th>Field</th><th>Value</th><th>RQ</th></tr></thead><tbody><tr><td>Venue type*</td><td>Journal or proceedings</td><td></td></tr><tr><td>Venue name*</td><td>Full name of venue</td><td></td></tr><tr><td>Publishing fate*</td><td>Year of publication</td><td>RQ1 &amp; RQ3</td></tr><tr><td>Author keywords*</td><td>List of keywords selected by authors</td><td></td></tr><tr><td>Feedback type</td><td>Direct or indirect</td><td></td></tr><tr><td>Feedback frequency</td><td>Frequency at which feedback is transmitted</td><td></td></tr><tr><td>Feedback methods</td><td>Feedback methodology(-ies) employed</td><td></td></tr><tr><td>Feedback medium</td><td>Medium by which feedback is received</td><td></td></tr><tr><td>Feedback features</td><td>Miscellaneous optional features implemented</td><td></td></tr><tr><td>Metrics &amp; metaphors</td><td>Quantifiable measurements and metaphors used to represent data</td><td>RQ2</td></tr><tr><td>Post-intervention study</td><td>Presence of a post-intervention study</td><td></td></tr><tr><td>Surveying</td><td>Questionnaires, focus groups and/or interviews</td><td></td></tr><tr><td>Survey participants</td><td>Number of participants surveyed</td><td></td></tr><tr><td>Survey data</td><td>Quantitative/qualitative data sorted into categories</td><td></td></tr><tr><td>Type of experiment</td><td>Simulation, Controlled or Field Experiment</td><td></td></tr><tr><td>Control group</td><td>Presence of a control Group</td><td></td></tr><tr><td>Duration of experiment</td><td>Duration in days</td><td></td></tr><tr><td>Participants</td><td>Total number of participants</td><td></td></tr><tr><td>General application field</td><td>General field meant to benefit from the publication</td><td></td></tr><tr><td>Specific application field</td><td>Specific field meant to benefit from the publication</td><td>RQ2 &amp; RQ3</td></tr><tr><td>Targeted actors</td><td>Actors meant to benefit from the publication</td><td></td></tr></tbody></table>

2. **Indicative feedback** is feedback with little to no processing and displayed either through a numerical value or real-time energy graph.

3. **Desegregated feedback** aims to break down energy consumption into finer levels of detail. In smart homes, overall usage can be divided by room and/or device—for instance, a house uses 100 units, with 40 from the kitchen, split into 30 for the oven and 10 for the refrigerator. This helps users target energy-saving efforts effectively.

4. **Ambient feedback** is feedback that directly alters the physical environment of the user and communicates through a variety of senses: Visual, hearing, touch or smell. For example, a LED equipped smart plug that communicates consumption or availability of renewable resources through the color displayed [26].

5. **Artistic feedback** conveys information using visual metaphors. For example, rather than showing energy use as numbers, it may be illustrated through stages of a wilting flower [27].

6. **Comparative feedback** allows for comparing consumption rates:

(a) **Normative feedback** compares user consumption to the average of similar users, such as households with similar size, age, and participants.

(b) **Historical feedback** compares current consumption to past rates, helping users set goals.

(c) **Peer feedback** compares user consumption to specific peers (e.g., friends, family), focusing on social comparison rather than fairness.

We also study the medium of feedback delivery (e.g., software, displays, mobile apps, websites, email, SMS), the metrics and metaphors used to convey information, and additional features in feedback systems. In this study, we have limited our focus to the most recurring features:

1. **Tips** are generic suggestions to help users reduce energy consumption. One system reviewed allows users to visualize their energy behavior and facilitates peer-to-peer management and sharing within the community. It provides tips, such as running appliances like dishwashers and washing machines during the day when renewable energy is most available [28].
2. **Recommendations** are personalized suggestions based on user context and behavior, guiding viable actions to reduce energy consumption. One system reviewed provides recommendations tailored to user energy use, such as suggesting turning off the television during other tasks based on how long the TV has been on [29].

3. **Forecasting** predicts future outcomes based on past and current behavior, helping individuals, organizations, and policymakers make informed decisions and design effective interventions. One system reviewed forecasts monthly energy consumption based on current trends, giving users an estimate of future consumption if current behaviors are maintained [30].

4. **Scheduling**, common in smart grids, manages electricity demand through techniques like load shifting, encouraging users to consume energy during off-peak hours. This reduces grid strain, balances resource use, and lowers carbon emissions. One system reviewed proposes an appliance scheduling engine that considers energy reduction goals and user habits when creating a schedule [31].

5. **Actuators** enable users or autonomous systems to remotely intervene. In a smart home, this could mean controlling appliances remotely, while in a PC setting, it could involve macros to adjust system settings or close processes. While this ease of intervention may reduce behavioral change, it requires less motivation. One system reviewed allows remote control of appliances through smart plugs via a web-based desegregated monitoring system [32].

6. **Goal-setting** involves establishing specific, measurable, achievable, relevant, and time-bound (SMART) objectives to guide behavior change. It motivates individuals to adopt positive changes in their actions, habits, or routines. One system allows users to set a savings goal and displays it as a reminder [33].

7. **Incentives** are rewards (material or token) used to motivate individuals to change their behavior. One system reviewed incorporates gamification into the feedback process, awarding points as tokens that can be redeemed for material rewards, such as gift certificates [34].

8. **Social interaction**, or *social influence*, uses social connections to encourage behavior change. This can involve peer pressure, group activities, shared goals, or learning by observing others. One system reviewed leverages social media to motivate environmentally responsible behavior, allowing users to compare their consumption to that of peers (e.g., Facebook friends) [35].

9. **Gamification** applies game design elements to non-game contexts to engage and motivate people toward specific goals. It incorporates aspects like incentives, in-game currency, rewards, and social interaction through features like leaderboards. One system reviewed designed a mobile game where in-game resources were tied to real-life environmentally responsible behaviors, such as reducing consumption or shifting usage [36].

10. **Notifications** deliver information to users at key moments to disrupt habits and drive behavior change. They can be integrated into an app or sent via email or SMS. In a smart home, users may receive notifications for forgotten tasks, goal deviations, or potential issues like energy leaks or peak consumption periods. One system sends phone notifications when users exceed their authorized energy consumption [37].

Finally, we will be interesting at studying to what extent proposed software approaches were tested through simulations, controlled or field experiments. In the case where human candidates are included and the object is behavioral change, we'll also study the presence of a post-intervention study, surveys, the types of qualitative data extracted, the presence of a control group, the duration and the number of participants.

The application field was determined manually by reading the publications and classified into four distinct fields: *Building*, *ICT*, *Industry* and *Transportation* that are respectively further split into more specific fields:

* *Energy management* (Smart-home applications, providing energy feedback in domestic or commercial settings), *Grid management* (Smart grid applications, demand response and renewable energy production) and *Consumer goods* (Consumer goods auditing).

* *Software* and *Hardware*.

* *Chain production, Manufacturing* and *Power quality*.

* *Electronic vehicles*.

These specific fields are further split into targeted actors who can be generalized as end users (residents, consumers, etc.), decision-makers (managers) and/or experts (researchers, technicians, etc.).
# 4. Analysis

## 4.1. RQ1: How has the research on energy feedback in software systems evolved over time, and what does it reveal about software's dual role in sustainability—both as a driver and a target?

In this section we are interested in studying the general trend of interest in providing energy feedback across time and fields of interests. To begin the analysis of trends in publications, we can start by analyzing the trend of publication

<figure><img src="image_5.png" /><figcaption>Figure 4: Number of publications per year and per venue type</figcaption></figure>

dates and venue types visualized on Figure 4. We can notice a sudden and sharp increase in publications in the year 2010 demonstrating a sudden interest in providing energy-related feedback in the years 2009/2010 with a majority of proceedings originating from conference proceedings up until 2021 where the number of journal publications maintain a stable trajectory but there is a sharp drop off of conference proceedings. First, it is important to consider the potential effects of the 2020 COVID-19 health crisis as a major disruptor. Lockdowns during this period led to the cancellation or postponement of various international sporting, cultural, and, by extension, academic events. This alone could, in part, explain the stark contrast observed in 2021 publication venues, where conference proceedings paled in comparison to journal publications—which, notably, saw a spike. Another variable to account for is the difficulty in accessing recent publications that remain behind paywalls. Of the 38 publications that passed the title/abstract screening but could not be fully accessed, 31 are from 2021 and 2022 (11 and 20 respectively). Considering that approximately half of the papers that passed the title/abstract screening were ultimately included in the corpus, an extrapolation suggests that around 5 additional papers from 2021 and 10 from 2020 would have been incorporated. Represented on Figure 4 as the *adjusted sum* curve, this would place 2020 and 2021 on par with prior years, leaving only 2023 as a noticeable drop not attributable to either the health crisis or limited access to recent publications. The observed drop in 2023 may be attributed to a combination of factors, including a shift in research priorities toward broader energy system challenges, such as decarbonization and digitalization, where energy feedback is treated as a sub-topic rather than a central focus. Additionally, evolving terminology may have excluded relevant studies from our corpus, while typical indexing and citation delays could mean that 2023 publications have yet to be fully captured. This trend is continued into 2024.

We have represented the most recurring venues in Table 5 (venues with more than one publication). We have found that 48 of the 235 publications (20.4%) originate from these venues. After studying the scopes of each venue listed, we found that a majority (63%) originates from venues broadly focused on the ICT sector with a few more specifically focused liked *Sensors*, *Conference on Human Factors in Computing Systems (CHI)*, or *Ubiquitous Computing (UbiComp)* that are respectively focused on sensors, the relation between users and digital systems and ubiquitous computing. While the previously mentioned conferences and journal fit the subject of energy feedback well, explaining the number of publications surveyed from the sources, they are still broad enough to neglect the energy aspect studied in this survey. Due to the majority of ICT oriented venues, one could naively believe that ICT is the targeted field of applications, which we can refute simply by analyzing the trend of author keywords.
<table><caption>**Table 5**
Publications per venue (over 1 publication)</caption><thead><tr><td>Venue name</td><td>Venue type</td><td>Amount</td></tr></thead><tbody><tr><td>Research Journal of Engineering Sciences (IJES)</td><td>Journal</td><td>4</td></tr><tr><td>Energy and Buildings</td><td>Journal</td><td>4</td></tr><tr><td>Sensors</td><td>Journal</td><td>3</td></tr><tr><td>USENIX Annual Technical Conference</td><td>Conference proceedings</td><td>3</td></tr><tr><td>Ubiquitous Computing (UbiComp)</td><td>Conference proceedings</td><td>3</td></tr><tr><td>International Journal of Innovative Technology and Exploring Engineering (IJITEE)</td><td>Journal</td><td>3</td></tr><tr><td>International Journal of Innovative Technology and Innovative Smart Grid Technologies (ISGT)</td><td>Conference proceedings</td><td>3</td></tr><tr><td>Journal of Computing in Civil Engineering</td><td>Journal</td><td>3</td></tr><tr><td>International Journal of Electrical and Computer Engineering (IJECE)</td><td>Journal</td><td>2</td></tr><tr><td>International Conference on Software Engineering (ICSE)</td><td>Conference proceedings</td><td>2</td></tr><tr><td>IEEE Transactions on Consumer Electronics</td><td>Journal</td><td>2</td></tr><tr><td>Euromicro Conference on Digital System Design (DSD)</td><td>Conference proceedings</td><td>2</td></tr><tr><td>IEEE Access</td><td>Journal</td><td>2</td></tr><tr><td>Energy Research & Social Science (ERSS)</td><td>Journal</td><td>2</td></tr><tr><td>Energies</td><td>Journal</td><td>2</td></tr><tr><td>Conference of Industrial Electronics Society (IECON)</td><td>Conference proceedings</td><td>2</td></tr><tr><td>Conference on Human Factors in Computing Systems (CHI)</td><td>Conference proceedings</td><td>2</td></tr><tr><td>Applied Energy</td><td>Journal</td><td>2</td></tr><tr><td>European Conference on Computer Systems (EuroSys)</td><td>Conference proceedings</td><td>2</td></tr></tbody></table>

<table><caption>**Table 6**
Most frequent author keywords (more than 10 occurrences)</caption><thead><tr><td>Author keyword</td><td>occurences</td></tr></thead><tbody><tr><td>smart grid</td><td>15</td></tr><tr><td>sustainability</td><td>13</td></tr><tr><td>energy monitoring</td><td>13</td></tr><tr><td>energy management</td><td>12</td></tr><tr><td>iot</td><td>12</td></tr><tr><td>smart home</td><td>11</td></tr><tr><td>demand response</td><td>11</td></tr><tr><td>energy efficiency</td><td>10</td></tr><tr><td>smart meter</td><td>10</td></tr><tr><td>Arduino</td><td>10</td></tr></tbody></table>

Through the study of the most recurring author keywords represented in Table 6, and by design of the survey, we find a majority of keywords describe measuring energy consumption and its visualization for end users through various feedback systems (Internet of things, energy management, eco-feedback, User interfaces, etc.). We can hypothesize from the remaining highest recurring keywords (demand response, smart grid and smart home) that the principal field of application will be the building sector.
<table><caption>**Table 7**
Publication count per feedback method and application field</caption><thead><tr><th></th><th></th><th>Indicative</th><th>Injunctive</th><th>Desegregated</th><th>Self (Historical)</th><th>Peer</th><th>Normative</th><th>Ambient</th><th>Artistic</th></tr></thead><tbody><tr><td rowspan="2">ICT</td><td>Amount</td><td>35</td><td>2</td><td>22</td><td>3</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Percentage</td><td>94.6%</td><td>5.4%</td><td>59.5%</td><td>8.1%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td rowspan="2">Building</td><td>Amount</td><td>161</td><td>42</td><td>79</td><td>92</td><td>6</td><td>8</td><td>14</td><td>5</td></tr><tr><td>Percentage</td><td>82.9%</td><td>21.6%</td><td>40.7%</td><td>47.4%</td><td>3.1%</td><td>4.1%</td><td>7.2%</td><td>2.6%</td></tr></tbody></table>

## 4.2. RQ2: What are the different forms and variations of feedback ? To what extent are these approaches tested?

In this section, we'll be analyzing and comparing the energy feedback systems present in the publications of the studied corpus according to the variables extracted and stated in Table 4 of the search process section. More specifically. Since only 4 out of the 236 articles did not fall under either the building or ICT fields of application, our analysis will focus on contrasting energy feedback systems within these two sectors, where a substantial number of papers allow for meaningful trend extraction. Although excluded from the trend analysis, the remaining articles are still included in Table 12 for reference.

We classified the proposed building and ICT feedback systems according to the classification scheme previously stated and represent them in tables 13, 14, 15, 16, 17, 18 and 19. By studying the targeted actors we found that, of the 194 publications targeting the building sector, 153 (78.9%) target at minimum the end user, which can vary from a resident in a smart home context to an occupant in a commercial or public building compared to the 28 (14.4%) that target decision makers (building managers) and the 12 (6.2%) that target experts (technicians). From this analysis we can determine that energy feedback systems tend to prioritize end users. When put side by side to the targeted actors in ICT where 10 (27%) target, end users ranging from office workers to computer lab students, 5 (13.5%) target decision makers (Data center operators, team leads), 31 (83.8%) target experts (developers, engineers or researchers), the contrast in priorities is clear. In the current state of the art, while energy feedback systems tend to target consumers in the building sector, in the ICT sector they tend to target experts, we believe this is significant in showing the maturity difference in raising awareness in these two sectors; while everyday users are aware of the energetic consumption of their household appliances, HVAC systems, water consumption and seek out feedback, experts in the ICT field are still relatively unaware of the energetic impact of their own field. This contrast in awareness is a cause of the bias of IT for green (Green by IT) over Green IT (Green in IT).

Feedback systems tend to be uniformly direct across the building and ICT sector, respectively 99% and 100% and immediate, respectively 98.4% and 94.6% (with the peculiarity of building feedback systems also being indirect in 8.1% of studied cases) The contrast between feedback systems in buildings and ICT reappear when studying the different feedback methods used which we summarized in Table 7. In the ICT feedback systems studied, we have encountered only 4 different feedback methods, principally indicative (94.6%) and desegregated (59.5%) feedback while in building feedback systems we have encountered all 8 different feedback methods with the addition of historical, injunctive and ambient feedback being significantly represented (respectively 47.4%, 21.6% and 7.2%). We believe the contrast in feedback methods used is a symptom of the contrast in audiences targeted. We have seen previously how the principal aim of feedback systems in ICT is to aid raise awareness in experts, where plain indicative feedback is sufficient with the addition of desegregated to help researchers and developers break down their software and hardware energy consumption to different levels of granularity. In buildings, where end users are targeted, while indicative and desegregated feedback is efficient for raising awareness, the addition of historical, injunctive, peer and normative feedback are supplemented to provide the persuasion and motivation needed to encourage environmental responsible behavior and reduce energy consumption.

To reduce their energy consumption, users require awareness, motivation and know-how. For the former, forecasting features allows users to better understand their consumption trends and the predicted outcome if their behavior remains unchanged while notifications allow for disrupting user behavior and providing key information at key moments. You
<table><caption>**Table 8**
Publication count per feature and per application field</caption><thead><tr><th></th><th></th><th>Forecasting</th><th>Recommendations</th><th>Scheduling</th><th>Incentives</th><th>Gamification</th><th>Social interaction</th><th>Goal-setting</th><th>Notifications</th><th>Tips</th><th>Actuators</th></tr></thead><tbody><tr><td rowspan="2">ICT</td><td>Amount</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Percentage</td><td>0%</td><td>2.7%</td><td>2.7%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>2.7%</td></tr><tr><td rowspan="2">Building</td><td>Amount</td><td>18</td><td>16</td><td>16</td><td>11</td><td>18</td><td>18</td><td>8</td><td>30</td><td>9</td><td>44</td></tr><tr><td>Percentage</td><td>9.3%</td><td>8.2%</td><td>8.2%</td><td>5.7%</td><td>9.3%</td><td>9.3%</td><td>4.1%</td><td>15.5%</td><td>4.6%</td><td>22.7%</td></tr></tbody></table>

will find the presence rates of these features presented in Table 8. To motivate the user, incentives, gamification, social interactions and goal setting are common and found in building feedback systems in, respectively, 5.7%, 9.3%, 9.3% and 4.1% of cases studied. For the latter, in their paper, *Adel Nouredinne et al.* [38] conducted an experiment on students providing indicative feedback on their energy consumption during a work exercise and found that 58.33% of students wanted to reduce their energy consumption but did not know how to. For this reason, it is also interesting to include tips or recommendations (ranging from generic boilerplate tips to personalized recommendations). The scheduling feature is mostly found in smart grid applications for shifting energy intensive activities to high availability periods to relieve the energetic strain on producers, produce financial gains and overall reduce the carbon impact. For this reason, we believe this feature would be more difficult to implement, as is, in the ICT sector to achieve carbon emission reductions, though could possibly be employed in data centers with very low time sensitive calculations. Nonetheless, we find ICT energy feedback systems lack these additional features which we consider salient for raising awareness, motivating and helping users achieve significant energy consumption reductions. Of the 37 ICT publications in this study, only one publication included features: Recommendations, scheduling and actuators. In their paper, *Hoi Chan and Jeffrey O. Kephart* [39] present a web-based green application which offers admins of a blade server the option to manually cap the energy consumption of hardware to achieve reductions in energy consumption at the cost of response time or proposes to do so automatically through predefined policies according to the desired response time.

Metrics displayed, on majority, are typical of energy feedback systems with Watts (W), Watthour (Wh), Joules (J) and carbon emissions in grams (gCO2) being the most present, respectively in 41.1%, 33.1%, 4.2% and 2.1%. It is interesting to note that while the usage of Watt is uniform between papers targeting building or ICT, the building sector tends to use Wh while ICT tends to use J. We believe this can be explained by the nature of energy consumption in both cases, with Wh more appropriate for the considerably higher consumption of entire households over J being more precise for the less significant consumption of ICT devices (For reference: 1 Wh = 3600 J). Another point of interest is the usage of currency and metaphors with only one of the 37 papers targeting ICT (2.7%) using currency [38] and one using metaphors [40] compared to the 37 of 194 (19.0%) and 4 of 194 (2.0%) in buildings that use currency and metaphors respectively. While we could justify this significant difference in the usage of currency the same as the difference in usage of Wh and J (cost savings are more significant on a household level than a device level), we also firmly believe this is partly due to the targeted actors of feedback systems. We believe that when consumers and end users are targeted, the use of currency as a metric is more persuasive than usual scientific metrics which are more pertinent to experts and academics conducting measurements. Additionally, we believe metaphors can be quite effective at digesting data and presenting it in a more user-friendly way, of the 5 publications that use metaphors, a majority of 80% aim solely consumers.

With an interest of understanding how thoroughly solutions are tested, we counted the amount of papers who applied their solution ranging from simulations to field studies in Table 9. We found that a majority (92.4%) of publications implemented and tested their proposal, ranging from controlled experiments or simulated data to deployment on the field with only 18 (7.6%) that do not explicit the implementation and testing of their prototype. When comparing these studies by application domain—specifically building versus ICT—we observe that 92.8% of the 194 building-related publications implemented and tested their proposals, compared to 94.6% of the 37 ICT-related publications, indicating no significant difference in the overall commitment to validation. However, a clearer contrast emerges in the type of experiments conducted: only 8.1% of ICT approaches involved field experiments, whereas 18.6% of those in the building domain did. This suggests a stronger inclination within the building sector to validate findings through more direct, real-world experimentation, despite the added complexity and constraints such methods entail. We argue that field experiments and surveys are especially pertinent when designing an energy feedback system directed at human
<table><caption>**Table 9**
Publication Count per Experiment Type</caption><thead><tr><th></th><th>Field of application</th><th>None</th><th>Simulation</th><th>Controlled experiment</th><th>Field experiment</th></tr></thead><tbody><tr><td rowspan="4">Publication count</td><td>Building</td><td>14</td><td>15</td><td>136</td><td>36</td></tr><tr><td>ICT</td><td>2</td><td>1</td><td>32</td><td>3</td></tr><tr><td>Industry</td><td>1</td><td>0</td><td>2</td><td>1</td></tr><tr><td>Transportation</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td></td><td><strong>TOTAL</strong></td><td><strong>18</strong></td><td><strong>16</strong></td><td><strong>170</strong></td><td><strong>40</strong></td></tr></tbody></table>

<table><caption>**Table 10**
Survey Results Across Application Fields and Evaluation Criteria</caption><thead><tr><th></th><th>Evaluation Criterion</th><th>Awareness</th><th>Census</th><th>Demographic</th><th>Design</th><th>Efficiency</th><th>Reasoning</th><th>Satisfaction</th><th>Usage</th></tr></thead><tbody><tr><td rowspan="3">Building</td><td>Survey</td><td>6</td><td>7</td><td>9</td><td>12</td><td>12</td><td>2</td><td>4</td><td>8</td></tr><tr><td>Focus Group</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Interview</td><td>2</td><td>2</td><td>2</td><td>10</td><td>6</td><td>3</td><td>5</td><td>5</td></tr><tr><td rowspan="3">ICT</td><td>Survey</td><td>2</td><td>0</td><td>1</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Focus Group</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Interview</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

end users. In the building sector, which we have seen to be more end user oriented, we have found that only 33 out of the 153 (21.6%) publications targeting human end users conducted field experiments, of which only 6 (18.2%) used control groups and only 6 (18.2%) continued monitoring post-intervention to determine whether or not the change was sustained or temporary.

When end users are the primary target, it becomes equally important to complement experimental validation with qualitative insights. Surveys, interviews, and focus groups offer valuable means of capturing user perspectives, helping to uncover not just what changes occur, but why they happen, how users interpret and engage with the system, and what contextual factors shape their behavior.

The surveys (questionnaires) analyzed in the corpus captured a range of qualitative data types, each contributing unique insights into user perspectives and contextual factors.

* **Awareness** data reflects participants' understanding of socio-economic or environmental issues, providing a sense of their baseline knowledge and engagement.

* **Census** data offers a snapshot of users' existing hardware or software infrastructure and their everyday energy consumption habits, helping to ground the findings in real-life conditions.

* **Demographic** data—including age, gender, education level, and political orientation—helps contextualize other responses and supports segmentation analysis.

* **Design** data gathers user feedback on both aesthetic and functional aspects of the system, suggesting avenues for improvement.

* **Efficiency** data focuses on participants' perceptions of how effective the solution is at meeting its intended goals, offering insight into user expectations and acceptance.

* **Reasoning** data delves into participants' thought processes, revealing motivations or justifications behind certain actions or reactions.

* **Satisfaction** data captures participants' emotional and experiential response to the solution.

* **Usage** data highlights interaction patterns such as frequency and duration of use.
We present in Table 10 the number of surveys, focus groups, and interviews conducted per field of application that extract qualitative data, as categorized above. Among the 194 building-related publications, 25 (12.9%) include surveys, 2 (1.0%) involve focus groups, and 16 (8.2%) report interviews. These proportions increase slightly when considering only end-user-centric studies, with 24 (15.7%) using surveys, 2 (1.3%) employing focus groups, and 16 (10.5%) incorporating interviews. In contrast, of the 36 ICT-related publications, only 3 (8.3%) include surveys, and none report the use of focus groups or interviews. We believe this highlights an opportunity for *Green for IT* approaches to place greater emphasis on end-user-centric qualitative data collection. While both fields leverage *design* and *efficiency* data—common in ICT and particularly prevalent in Human-Computer Interaction (HCI) through models like the Technology Acceptance Model (TAM)—they tend to overlook deeper investigations into user habits and motivations. Specifically, they make limited use of *census*, *reasoning*, and *usage* types of inquiries.

Energy data collection in ICT systems spans a spectrum from mobile devices to data centers, employing both measurement and estimation techniques tailored to the hardware and user expertise. For mobile devices, energy consumption is typically measured using hardware-based tools such as power meters (e.g., Monsoon FTA22D) [41], or estimated through software-based methods that analyze system metrics like CPU usage and execution traces [42]. Tools like BatteryStatsInfo and the Power Profiler API support developers in logging and analyzing power usage data [40]. In server and IoT environments, energy data collection commonly involves software-based estimation using system-level power models and usage statistics. For example, tools like Microsoft Joulemeter and Green Tracker estimate energy consumption by tracking system activity and automating usage patterns [43, 44]. Measurement can also be performed with hardware-based methods, such as external smart meters or on-board sensors, to directly monitor power usage. Many modern approaches combine both estimation and measurement, using software models calibrated with hardware data to improve accuracy. These methodologies are designed for a wide range of users, from end-users employing lightweight tools to developers and experts performing in-depth analysis using instrumentation, regression modeling, and API-level monitoring. This flexibility ensures applicability across varied ICT contexts, delivering actionable insights into energy consumption for stakeholders at all levels.

### 4.3. RQ3: What are the existing software approaches for providing feedback to users on their software energy consumption?

In this section, we will be uniquely interested in energy feedback systems in ICT and more specifically on software energy consumption and comparing these feedback approaches.

After analyzing the content, we have classified each publication according to the general field of expertise, the specific target(s) for improvement, and the specific actor(s) targeted and we take into consideration the author's own explicit classification which we complete with our own opinion when left incomplete. Aside from a single publication that we considered to be a hybrid between building and transportation, all publications have exclusive general fields. This exclusivity between classes is lost when classifying specific targets and actors. Notable examples of this are energy feedback systems for smart homes with renewable energy production that have the dual objective of smart grid management / demand response and home energy consumption management. The result of our classification can be seen in detail on the Table 11.

Confirming the hypothesis of the previous section, we found that a great majority of 194 out of 236 publications (82.2%) publications in our study corpus target the building sector demonstrating the bias for *IT for green* (Green by IT) over *Green IT* (Green in IT) with a majority of works targeting consumers and prosumers in grid and building management contexts, but a significant minority aiming to assist energy providers (notably in measuring electricity quality, billing and theft prevention), facility managers for commercial or building portfolio management and non-intrusive load monitoring (NILM).

Nonetheless, while ICT is targeted in a minority of cases (15.7%), it is still significant especially relative to the industry and transportation sector. The industry sector is sparsely represented in this survey with the works of Vasco Delgado-Gomes et al. [45] and Thomas Schmitt et al. [46] that proposes an energy monitoring infrastructure to determine which phases of manufacturing are most ripe for optimizations with the addition of Yingying Seow et al. [47] that proposes a simulation software to help estimate the resource cost of manufacturing processes. The transportation sector in this survey is exclusively electronic vehicles (EV) with one paper including EV charging ports into their residential smart
<table><caption>**Table 11**
Publications per application field and per actor</caption><thead><tr><th>General field</th><th>Specific target</th><th>Targeted actors</th><th>Amount</th></tr></thead><tbody><tr><td rowspan="6">Building</td><td rowspan="3">Management</td><td>Occupant</td><td>151</td></tr><tr><td>Manager</td><td>20</td></tr><tr><td>NILM</td><td>20</td></tr><tr><td rowspan="3">Grid</td><td>Prosumer</td><td>43</td></tr><tr><td>Manager</td><td>18</td></tr><tr><td>Providers</td><td>16</td></tr><tr><td>Consumer</td><td>Auditor</td><td>1</td></tr><tr><td rowspan="6">ICT</td><td rowspan="3">Software</td><td>User</td><td>8</td></tr><tr><td>Dev</td><td>21</td></tr><tr><td>Manager</td><td>1</td></tr><tr><td rowspan="3">Hardware</td><td>User</td><td>6</td></tr><tr><td>Dev</td><td>6</td></tr><tr><td>Manager</td><td>3</td></tr><tr><td rowspan="3">Industry</td><td>Chain production</td><td>Manager</td><td>1</td></tr><tr><td>Manufacture</td><td>Manager</td><td>1</td></tr><tr><td>Power quality</td><td>Manager</td><td>1</td></tr><tr><td rowspan="2">Transportation</td><td rowspan="2">EV</td><td>User</td><td>1</td></tr><tr><td>Manager</td><td>1</td></tr></tbody></table>

grid proposition [48] and the other exclusively proposing software for collecting, controlling, and visualizing power and electrified transportation systems [49].

Within the ICT corpus, there is a tendency of focusing software optimizations through feedback with approximately twice as many publications focusing software reductions over hardware reductions. By software energy consumption reductions, we account for reduced software energy consumption due to optimized code and behavioral change (usage of optimized software, reduced usage, etc.). For hardware energy consumption reduction, we consider hardware improvements (better CPUs, cooling technology, etc.) and the reduced use of hardware systems as a whole due to user behavior modifications (e.g., better idle times, etc.).

For ICT hardware improvements, the target audience for improvements seems to be quite uniform between end users, experts and decision makers. An example of the latter would be the paper of Lide Zhang et al. in which they present PowerBooster and PowerHelper (temporary placeholder name) which, respectively, automatically generates an energy consumption model through integrated battery sensors and battery drain knowledge to monitor energy consumption of mobile phones and PowerHelp as a more user-friendly GUI that uses the latter’s models to deliver to users the data they require to make energy-centric choices between competing applications [41]. For experts, Muthuraman Thangaraj and Subramanian Anuradha [50] put forward their integrated energy management framework called Aatral with the aim of enabling energy optimizations in the field of wireless sensor networks through the ability to monitor and optimize energy data. For the former, we can take the example on the works of Guoming Tang et al. that propose a software-based NILM tool for fine-grained monitoring of server energy consumption in data centers from racks down to hardware components [51].

For ICT software improvements, the target audience is skewered for developers with three times the number of publications aiming to contribute to experts (78.4%) over users (35.1%). Even when end users are mentioned as a potential benefactor of contributions, the sheer complexity of the tool makes it only an option for the tech-savvy contingent of users. There is also a rift in the medium of targeted optimizations between mobile and PC software with
a majority of publications targeting energy efficiency in mobile computing (45.9%) followed by PCs (32.4%), servers and virtual machines (18.9%). This focus on mobile computing and optimizations of applications could be explained by the recently increasing importance consumers put on the battery life of smartphones and shows the room for more publications on PC software on the end-user spectrum, where battery life is less of a concern. Of the 9 publications that target PC software optimizations, only 4 of which target end users, the rest of which target developers. In all four of those papers, energy consumption is calculated through hardware components usage, especially CPU [44]. Of these papers only two study the impact of software energy feedback on the user [38] [52].

## 4.4. Findings & recommendations

To recap, the aim of this systematic mapping survey study is to discuss the role of feedback in driving behavioral change and reducing energy consumption. We also aim to highlight the shortcomings of energy feedback systems in ICT, notably on a software level, and to provide researchers with a state-of-the-art overview of energy feedback systems in software engineering—as both a driving force and a patient for sustainability. To this aim, we have regrouped and summarized what we believe to be the pertinent findings and explicitly highlight the associated recommendations for future works:

* **Finding:** ICT is often viewed as a driver of sustainability (IT for Green / Green by IT) and less frequently as a patient of sustainability (Green IT / Green in IT), with only 37 of the 236 publications (15.7%) intervening in ICT.

**Recommendation:** We recommend increasing research efforts on providing energy feedback in ICT, which is an increasingly energy-intensive sector.

* **Finding:** In the building sector, energy feedback systems primarily target end users, whereas in ICT, this trend is reversed—only a minority of publications do so (78.9% vs. 35.1%).

**Recommendation:** We recommend increasing efforts to raise awareness among end users in ICT, rather than focusing solely on researchers and developers.

* **Finding:** Energy feedback systems in buildings provide a variety of feedback types—injunctive (21.6%), historical (47.4%), peer (3.1%), normative (4.1%), and artistic (2.6%). In contrast, ICT systems rarely include these types: only 2 papers (5.4%) used injunctive feedback, and 3 (8.1%) used historical feedback. Furthermore, nearly no ICT systems implemented the specific additional features identified in this study.

**Recommendation:** We recommend that future ICT energy feedback systems integrate persuasive and user-friendly feedback mechanisms, drawing from the HCI strategies of smart-home systems, to enhance engagement and energy reduction effectiveness.

* **Finding:** Although 92.3% of publications reported positive outcomes, only 16.2% conducted field experiments. Among 153 building-related publications targeting end users, just 21.6% included field experiments, and of those, only 18.2% used control groups or post-intervention monitoring. In the ICT domain, only two out of eight publications targeting end-users conducted field experiments. Of these, only one included control groups, and neither assessed post-intervention effects.

**Recommendation:** We recommend conducting rigorous field experiments with human participants, adhering to methodological best practices such as randomized control groups, justified sample sizes, demographic documentation, and long-term follow-up.

* **Finding:** Surveys, questionnaires, and interviews were used in only 22.2% of building publications that targeted end users. However, among ICT papers targeting end users, 50.0% used such methods, albeit with a small sample size.

**Recommendation:** We highly recommend incorporating surveys and qualitative user studies when human participants are involved in order to:

- Gather direct feedback on user preferences, needs, and expectations.

- Understand how users interact with systems and which features they prioritize.

- Identify pain points, challenges, and user frustrations.

- Assess overall user satisfaction and perception of systems.

- Explore demographic and cultural factors affecting user interaction and inclusivity.
**5. Threats to validity**

As with any systematic mapping study, several potential threats to validity may affect our findings. We have attempted to mitigate these risks through methodological rigor and transparency, but some limitations remain:

* **Search Strategy Bias.** The comprehensiveness of the corpus depends heavily on the quality of the search string. Despite refining the query using Term Frequency-Inverse Document Frequency (TF-IDF) analysis and supplementing with backward snowballing, it is possible that relevant publications using divergent terminology or published in lesser-indexed venues were omitted.

* **Selection and Filtering Bias.** Paper selection, particularly during title, abstract, and full-text screening, can be subject to human judgment errors. To limit this, all filtering steps were performed by at least two researchers, and disagreements were resolved collaboratively. Nonetheless, human error cannot be fully excluded.

* **Data Extraction Bias.** Manual data extraction introduces a risk of misclassification or omission. We mitigated this through the use of a structured data extraction form and cross-verification between researchers. However, certain variables, such as targeted actors or feedback methods, may still be prone to subjective interpretation, especially in ambiguous or sparsely described cases.

* **Publication Bias.** Our study includes only peer-reviewed and publicly accessible literature in English. As such, non-English or gray literature (e.g., technical reports, theses) may contain relevant contributions that are not reflected in our analysis.

* **Temporal Validity.** The final search was conducted on April 5, 2024. Due to the rapid evolution of the field, recent or in-press works may not have been captured. While a final update was performed close to the writing date, indexing delays and access restrictions (e.g., paywalls) may have led to the exclusion of some recent publications.

* **Snowballing Scope.** Although backward snowballing added valuable references, forward snowballing (using citation chaining) was not performed due to practical constraints. This may have limited the inclusion of newer papers citing the core publications.

* **Generalizability.** The classification and recommendations are derived from a specific corpus focused on software-based energy feedback systems. Results may not be generalizable to hardware-centric energy efficiency studies or to broader sustainability contexts not involving feedback mechanisms.

Despite these limitations, we believe our study provides a meaningful overview of the current state of software-based energy feedback systems, and we have taken care to ensure transparency, reproducibility, and methodological robustness throughout the research process.

**6. Conclusion**

In conclusion, we aimed at conducting a systematic mapping study on the state of software publications that provides energy feedback in the aim of achieving energy consumption efficiency. We have conducted our search across 5 different databases: IEEE, ACM, Web of Science, Science Direct and Scopus. Through manual filtration according to our defined inclusion and exclusion criteria and snowballing, we were able to reduce the corpus from 11617 results to 236 publications.

The aim was to study the trends and limits of the current state of the art and found a recent loss of interest in conference proceedings. We found that a majority of energy feedback systems are designed in the building sector with
software playing the central role for sustainability rather than the patient (RQ1). Of the remaining significant minority of publications aiming to achieve energy efficiency, we found a majority to focus on software energy feedback of which a majority focuses on experts (developers and researchers). From this we have made the observation that while raising software energy consumption awareness is a concern amidst experts, it has not yet been emulated in end users and consumers (RQ3).

Additionally we have found that while a majority of publications implemented and tested their contributions only a minority of publications did so through field experiments of which few had control groups and/or continued monitoring post-intervention to study whether or not energy reductions could be sustained. Furthermore, we found few publications conducted surveys under the form of questionnaires, focus groups and interviews on human candidates (RQ2).

From the previous findings, we recommended the adaptation of persuasive technologies and feedback methods already tested in smart home and grid applications to ICT with the aim of raising awareness to software energy consumption in end users and consumers and to thoroughly test these approaches through rigorous field experiments following state-of-the-art protocols with accompanying surveys to further supplement the amount of quantitative and qualitative produced and offer new insights and how to optimize software energy consumption reductions.

## Acknowledgments

The research leading to this publication is supported by the French Research Agency through the ANR-22-CE25-0010 BEHAVE project.

## Notes

The corpus data can be accessed at https://zenodo.org/records/15194748.

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<table><caption>Table 12: Feedback system classification - Industry & Transportation</caption><thead><tr><th>Field</th><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Method(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>Industry</td><td>[45]</td><td>Decision makers</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Display and web</td><td>VA</td><td>None</td></tr><tr><td>Industry</td><td>[46]</td><td>Decision makers</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>Wh</td><td>None</td></tr><tr><td>Industry</td><td>[47]</td><td>Decision makers</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Web</td><td>J</td><td>None</td></tr><tr><td>Industry</td><td>[53]</td><td>Decision makers</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>V</td><td>None</td></tr><tr><td>Transportation</td><td>[49]</td><td>Decision makers</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Web</td><td>EVR Load</td><td>None</td></tr></tbody></table>
<table><caption>Table 13: Feedback system classification - Buildings</caption><thead><tr><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Method(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>[54]</td><td>Decision maker</td><td>Direct</td><td>Report</td><td>Historical</td><td>Web</td><td>UNSPEC</td><td>None</td></tr><tr><td>[55]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated and historical</td><td>Software</td><td>Wh</td><td>None</td></tr><tr><td>[56]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>VAR and W</td><td>Actuators and scheduling</td></tr><tr><td>[57]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>T and Wh</td><td>None</td></tr><tr><td>[58]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Web</td><td>Wh</td><td>None</td></tr><tr><td>[59]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated and historical</td><td>Web</td><td>W</td><td>Actuators and scheduling</td></tr><tr><td>[60]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Mobile</td><td>W</td><td>Actuators and scheduling</td></tr><tr><td>[61]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>Va and W</td><td>Actuators and scheduling</td></tr><tr><td>[31]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and injunctive</td><td>Mobile</td><td>Currency and W</td><td>Actuators, forecasting, notifications, recommendations and scheduling</td></tr><tr><td>[62]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and injunctive</td><td>Web</td><td>Currency, Temp and W</td><td>Actuators, forecasting, notifications, recommendations and scheduling</td></tr><tr><td>[63]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Web</td><td>Wh</td><td>None</td></tr><tr><td>[64]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Mobile</td><td>Currency and Wh</td><td>Actuators</td></tr><tr><td>[30]</td><td>Auto and decision maker</td><td>Direct and indirect</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>A, currency, gC, PF, VAR, V, W and Wh</td><td>Forecasting and notifications</td></tr><tr><td>[65]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software and web</td><td>AP, PF, VAR and W</td><td>Actuators</td></tr><tr><td>[66]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[67]</td><td>Auto and decision maker</td><td>Direct and indirect</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Software</td><td>Currency</td><td>Actuators, notifications and recommendations</td></tr><tr><td>[68]</td><td>Auto, end user and decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>Temp and Wh</td><td>Actuators, forecasting, goal setting, recommendations and scheduling</td></tr><tr><td>[69]</td><td>End user and expert</td><td>Direct and indirect</td><td>Immediate</td><td>Historical and indicative</td><td>Display and mobile</td><td>A, PF, V, VAR, W and Wh</td><td>Notifications</td></tr><tr><td>[70]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>Wh</td><td>None</td></tr><tr><td>[71]</td><td>End user</td><td>Direct and indirect</td><td>Immediate</td><td>Historical</td><td>Web</td><td>Wh</td><td>Notifications</td></tr><tr><td>[72]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>Humidity, sound, temp and Wh</td><td>Actuators</td></tr><tr><td>[73]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Mobile</td><td>Wh</td><td>Actuators</td></tr><tr><td>[74]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>Temp and W</td><td>None</td></tr><tr><td>[75]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and injunctive</td><td>Software</td><td>A, PF, V and Wh</td><td>None</td></tr><tr><td>[76]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and historical</td><td>Mobile</td><td>Currency and Wh</td><td>Actuators</td></tr><tr><td>[77]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>A, PF, V, VA and W</td><td>Actuators</td></tr><tr><td>[78]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile and web</td><td>A, V and W</td><td>Forecasting, recommendations and scheduling</td></tr><tr><td>[79]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>UNSPEC</td><td>None</td></tr><tr><td>[80]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>Forecasting and notifications</td></tr><tr><td>[81]</td><td>Auto</td><td>Direct and indirect</td><td>Immediate</td><td>Indicative</td><td>Display</td><td>I, P, PF, Currency, V and Wh</td><td>Forecasting and notifications</td></tr><tr><td>[82]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and historical</td><td>Web</td><td>Temp, W and Wh</td><td>None</td></tr><tr><td>[83]</td><td>Decision maker and end user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>PF and W</td><td>Notifications and recommendations</td></tr><tr><td>[84]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile</td><td>Wh</td><td>None</td></tr><tr><td>[85]</td><td>End user</td><td>Direct and indirect</td><td>Immediate</td><td>Indicative and historical</td><td>Mobile and web</td><td>Wh</td><td>Actuators and notifications</td></tr><tr><td>[86]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>W</td><td>Actuators and recommendations</td></tr></tbody></table>
<table><caption>Table 14: Feedback system classification - Buildings</caption><thead><tr><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Mmethod(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>[87]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display</td><td>A, V, VA and VAR</td><td>None</td></tr><tr><td>[88]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>W and Wh</td><td>Actuators</td></tr><tr><td>[89]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>Wh</td><td>None</td></tr><tr><td>[90]</td><td>End user</td><td>Direct and indirect</td><td>Immediate</td><td>Indicative</td><td>Display and web</td><td>PF, VA, VAR, W and Wh</td><td>Notifications</td></tr><tr><td>[91]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and injunctive</td><td>Web</td><td>Humidity, temp and W</td><td>None</td></tr><tr><td>[92]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Mobile</td><td>gC and Wh</td><td>Forecasting, gamification and social interactions</td></tr><tr><td>[93]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Display and web</td><td>W</td><td>Forecasting, gamification and social interactions</td></tr><tr><td>[94]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>Currency, Wh and usage</td><td>None</td></tr><tr><td>[33]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and injunctive</td><td>Display</td><td>Currency, gC, metaphors and Wh</td><td>Gamification, goal setting and social interactions</td></tr><tr><td>[95]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>Currency, gC, metaphors and Wh</td><td>Gamification, goal setting and social interactions</td></tr><tr><td>[96]</td><td>Auto and expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display</td><td>A, J and W</td><td>None</td></tr><tr><td>[97]</td><td>End user</td><td>Indirect</td><td>Immediate</td><td>Ambient</td><td>None</td><td>None</td><td>None</td></tr><tr><td>[98]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Web</td><td>Currency, gCO2, W and Wh</td><td>Notifications</td></tr><tr><td>[99]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>Battery, humidity and temp</td><td>None</td></tr><tr><td>[100]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>A and Wh</td><td>None</td></tr><tr><td>[101]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Display</td><td>A, lux, occupancy, PF, temp, V, W, and Wh</td><td>None</td></tr><tr><td>[102]</td><td>Auto and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Display</td><td>VAR and W</td><td>Notifications</td></tr><tr><td>[103]</td><td>End user</td><td>Direct and indirect</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Web</td><td>W</td><td> GAMIFICATION AND SOCIAL INTERACTIONS</td></tr><tr><td>[27]</td><td>End user</td><td>Indirect</td><td>Immediate</td><td>Artistic and historical</td><td>Web</td><td>None</td><td>Forecasting, gamification and social interactions</td></tr><tr><td>[104]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>Humidity, Hz, temp, V and Wh</td><td>Actuators</td></tr><tr><td>[105]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>Currency, VA, VAR, W and Wh</td><td>Actuators</td></tr><tr><td>[106]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>A, PF, V, VAR and W</td><td>Actuator</td></tr><tr><td>[107]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and injunctive</td><td>Web</td><td>Currency and W</td><td>Actuators, recommendations and scheduling</td></tr><tr><td>[108]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient, artistic and indicative</td><td>Display</td><td>None</td><td>Actuators and social interactions</td></tr><tr><td>[109]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile</td><td>A, humidity, temp and W</td><td>Actuators</td></tr><tr><td>[28]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and normative</td><td>Software</td><td>A, battery, W and Wh</td><td>Gamification, recommendations, scheduling, social interactions and tips</td></tr><tr><td>[110]</td><td>Auto and end user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>gC and Wh</td><td>Actuators, forecasting, recommendations and scheduling</td></tr><tr><td>[111]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and injunctive</td><td>Mobile</td><td>Humidity, lux, temp and W</td><td>None</td></tr><tr><td>[112]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Web</td><td>Temp and Wh</td><td>Actuators</td></tr><tr><td>[113]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Web</td><td>Temp and Wh</td><td>Actuators</td></tr><tr><td>[34]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative, injunctive and peer</td><td>Web</td><td>W and Wh</td><td>Gamification, incentives and social interactions</td></tr><tr><td>[114]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Display and mobile</td><td>Currency, gCO2 and Wh</td><td>None</td></tr><tr><td>[115]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient</td><td>None</td><td>None</td><td>None</td></tr><tr><td>[116]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Mobile and software</td><td>W</td><td>None</td></tr></tbody></table>
<table><caption>Table 15: Feedback system classification - Buildings</caption><thead><tr><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Method(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>[117] and decision maker</td><td>Auto, end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Display and web</td><td>Currency and Wh</td><td>Actuators, forecasting and recommendations</td></tr><tr><td>[118]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Software</td><td>Currency, RT, W and Wh</td><td>None</td></tr><tr><td>[119]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Mobile</td><td>A, currency and W</td><td>Actuators</td></tr><tr><td>[120]</td><td>End user</td><td>Direct and indirect</td><td>Immediate</td><td>Historical and indicative</td><td>Mobile and web</td><td>Currency</td><td>Actuators and notifications</td></tr><tr><td>[121]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display and software</td><td>A and V</td><td>None</td></tr><tr><td>[122]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Historical</td><td>Web</td><td>W</td><td>None</td></tr><tr><td>[123]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Mobile</td><td>gCO2, humidity, temp and WH</td><td>Goal setting and tips</td></tr><tr><td>[124]</td><td>Auto and end user</td><td>Direct and indirect</td><td>Immediate</td><td>Desegregated and indicative</td><td>Mobile</td><td>J and W</td><td>Actuators, notifications and recommendations</td></tr><tr><td>[125]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical</td><td>Software</td><td>Wh</td><td>None</td></tr><tr><td>[126]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>W</td><td>Actuators, notifications and recommendations</td></tr><tr><td>[36]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Injunctive</td><td>Mobile</td><td>None</td><td>Gamification and incentives</td></tr><tr><td>[127]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[128]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>A, V, VAR and W</td><td>Actuators and notifications</td></tr><tr><td>[129]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient and injunctive</td><td>None</td><td>None</td><td>None</td></tr><tr><td>[130]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>W and Wh</td><td>None</td></tr><tr><td>[131]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Web</td><td>Currency, gCO2 and Wh</td><td>None</td></tr><tr><td>[132]</td><td>Decision maker</td><td>Direct</td><td>Report</td><td>Indicative</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[133]</td><td>Decision maker and expert</td><td>Direct and indirect</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>PQ</td><td>Notifications</td></tr><tr><td>[134]</td><td>Decision maker and expert</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>PQ</td><td>Notifications</td></tr><tr><td>[135]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[136]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[137]</td><td>End user and decision maker</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and injunctive</td><td>Web</td><td>W</td><td>None</td></tr><tr><td>[138]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile and web</td><td>Wh</td><td>None</td></tr><tr><td>[139]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>Wh</td><td>None</td></tr><tr><td>[140]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>W</td><td>None</td></tr><tr><td>[141]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile and web</td><td>W</td><td>Notifications</td></tr><tr><td>[142]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[143]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>Currency, gCO2 and W</td><td>None</td></tr></tbody></table>
<table><caption>Table 16: Feedback system classification - Buildings</caption><thead><tr><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Method(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>[144]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[145]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[146]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[147]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[148]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[149]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile</td><td>None</td><td>Tips</td></tr><tr><td>[150]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and injunctive</td><td>Mobile and software</td><td>None</td><td>None</td></tr><tr><td>[151]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Software</td><td>A, kW/m2, temp and W</td><td>None</td></tr><tr><td>[152]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and injunctive</td><td>Display</td><td>A, W and Wh</td><td>None</td></tr><tr><td>[153]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[154]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>A, V and W</td><td>None</td></tr><tr><td>[155]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and injunctive</td><td>Software</td><td>A, V and W</td><td>None</td></tr><tr><td>[156]</td><td>End user</td><td>Direct and indirect</td><td>Short-term</td><td>Historical and indicative</td><td>Mobile and web</td><td>Temp, W and Wh</td><td>None</td></tr><tr><td>[157]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Web</td><td>Wh</td><td>Actuators</td></tr><tr><td>[158]</td><td>End user</td><td>Direct and indirect</td><td>Immediate</td><td>Indicative</td><td>Mobile, software and web</td><td>Currency, PF, V and Wh</td><td>None</td></tr><tr><td>[159]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Web</td><td>VAR and W</td><td>None</td></tr><tr><td>[160]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>A, PF, V, W and Wh</td><td>None</td></tr><tr><td>[161]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[162]</td><td>End user</td><td>Direct and indirect</td><td>Immediate</td><td>Indicative and injunctive</td><td>Display and mobile</td><td>A and Wh</td><td>Actuators and notifications</td></tr><tr><td>[163]</td><td>Auto and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>Actuators and scheduling</td></tr><tr><td>[164]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and injunctive</td><td>Web</td><td>A and V</td><td>Actuators and scheduling</td></tr><tr><td>[165]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>Currency and W</td><td>Actuators</td></tr><tr><td>[166]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[167]</td><td>Auto and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>Humidity, lux, temp</td><td>Actuators</td></tr><tr><td>[168]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Software</td><td>A, V and W</td><td>None</td></tr><tr><td>[169]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>Actuators</td></tr><tr><td>[170]</td><td>Decision maker and expert</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>W and Wh</td><td>None</td></tr><tr><td>[171]</td><td>Auto and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Mobile</td><td>Wh</td><td>Actuators</td></tr><tr><td>[172]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Mobile and web</td><td>W</td><td>Actuators</td></tr></tbody></table>
<table><caption>Table 17: Feedback system classification - Buildings</caption><thead><tr><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Method(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>[172]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Mobile and web</td><td>W</td><td>Actuators</td></tr><tr><td>[173]</td><td>End user and auto</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display and mobile</td><td>Wh</td><td>Actuators, forecasting, goal setting and notifications</td></tr><tr><td>[174]</td><td>End user and auto</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>Va</td><td>Forecasting and scheduling</td></tr><tr><td>[175]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display and mobile</td><td>V</td><td>Notifications</td></tr><tr><td>[176]</td><td>End user and decision maker</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>UNSPEC</td><td>None</td></tr><tr><td>[177]</td><td>Auto and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>UNSPEC</td><td>Actuators</td></tr><tr><td>[178]</td><td>End user and expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>A and V</td><td>None</td></tr><tr><td>[179]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile</td><td>W and Wh</td><td>None</td></tr><tr><td>[180]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display</td><td>V</td><td>None</td></tr><tr><td>[181]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Display</td><td>Wh</td><td>None</td></tr><tr><td>[182]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Mobile</td><td>UNSPEC</td><td>Notifications</td></tr><tr><td>[183]</td><td>Auto and expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software and web</td><td>RMS</td><td>None</td></tr><tr><td>[184]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Software</td><td>UNSPEC</td><td>Actuators, forecasting and scheduling</td></tr><tr><td>[185]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and injunctive</td><td>Display</td><td>UNSPEC</td><td>None</td></tr><tr><td>[186]</td><td>Auto and end user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and injunctive</td><td>Web</td><td>W</td><td>Actuators, forecasting and scheduling</td></tr><tr><td>[48]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>Wh</td><td>None</td></tr><tr><td>[187]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>RMS</td><td>None</td></tr><tr><td>[188]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[189]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and normative</td><td>Web</td><td>Wh</td><td>Gamification, goal setting, incentives, notifications, social interactions and tips</td></tr><tr><td>[190]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Artistic and injunctive</td><td>Display</td><td>None</td><td>None</td></tr><tr><td>[191]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[192]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Display</td><td>Wh</td><td>None</td></tr><tr><td>[193]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[194]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient and injunctive</td><td>None</td><td>Currency and Wh</td><td>None</td></tr><tr><td>[195]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Injunctive</td><td>Web</td><td>VA, VAR and W</td><td>Forecasting, recommendations and tips</td></tr><tr><td>[196]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>None</td><td>None</td></tr><tr><td>[197]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>None</td><td>Currency, gCO2 and Wh</td><td>None</td></tr><tr><td>[198]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient, historical, indicative and injunctive</td><td>Display</td><td>Unspec</td><td>Incentives</td></tr><tr><td>[199]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient, indicative and injunctive</td><td>None</td><td>W</td><td>Gamification and incentives</td></tr><tr><td>[200]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Mobile</td><td>None</td><td>Forecasting, gamification and incentives</td></tr><tr><td>[201]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and normative</td><td>Web</td><td>None</td><td>Gamification</td></tr><tr><td>[202]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and injunctive</td><td>Software</td><td>None</td><td>Gamification and incentives</td></tr><tr><td>[203]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and injunctive</td><td>Software</td><td>None</td><td>Gamification and incentives</td></tr><tr><td>[204]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>Wh</td><td>Gamification, incentives and social interactions</td></tr><tr><td>[205]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display and mobile</td><td>Currency and Wh</td><td>None</td></tr></tbody></table>
<table><caption>Table 18: Feedback system classification - Buildings</caption><thead><tr><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Method(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>[206]</td><td>Decision maker</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>A, V, W, Wh</td><td>None</td></tr><tr><td>[207]</td><td>Auto</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Web</td><td>W</td><td>None</td></tr><tr><td>[208]</td><td>End user and expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>PQ</td><td>None</td></tr><tr><td>[37]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile and web</td><td>Wh</td><td>Notifications</td></tr><tr><td>[209]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Mobile and software</td><td>Wh</td><td>Notifications</td></tr><tr><td>[210]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated</td><td>Software</td><td>A, Hz and V</td><td> GAMIFICATION, social interactions and tips</td></tr><tr><td>[211]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>UNSPEC</td><td>None</td></tr><tr><td>[212]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Artistic</td><td>Web</td><td>gCO2 and metaphors</td><td>None</td></tr><tr><td>[213]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient, indicative and injunctive</td><td>Software</td><td>None</td><td> GAMIFICATION and social interaction</td></tr><tr><td>[214]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display</td><td>Humidity and temp</td><td> GAMIFICATION and social interaction</td></tr><tr><td>[215]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Mobile</td><td>A, V and W</td><td> GAMIFICATION and social interaction</td></tr><tr><td>[35]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative, injunctive, normative and peer</td><td>Mobile</td><td>Metaphor</td><td> GAMIFICATION and social interaction</td></tr><tr><td>[32]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>Actuators</td></tr><tr><td>[216]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient and indicative</td><td>None</td><td>None</td><td>None</td></tr><tr><td>[26]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient and indicative</td><td>None</td><td>None</td><td>Notifications</td></tr><tr><td>[217]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>Currency and Wh</td><td>Incentives and social interactions</td></tr><tr><td>[218]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>Currency and Wh</td><td>Actuators and scheduling</td></tr><tr><td>[219]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Mobile and web</td><td>UNSPEC</td><td>None</td></tr><tr><td>[220]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>Currency and Wh</td><td>None</td></tr><tr><td>[221]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and injunctive</td><td>Mobile and web</td><td>Currency, W and Wh</td><td>None</td></tr><tr><td>[222]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and injunctive</td><td>Mobile</td><td>W</td><td> GAMIFICATION and tips</td></tr><tr><td>[223]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and injunctive</td><td>Mobile</td><td>W</td><td> GAMIFICATION and tips</td></tr><tr><td>[224]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient, indicative and injunctive</td><td>None</td><td>None</td><td>None</td></tr><tr><td>[225]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and normative</td><td>UNSPEC</td><td>Currency, gCO2, W and Wh</td><td> Social interaction and tips</td></tr><tr><td>[226]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative, normative and peer</td><td>Web</td><td>gCO2</td><td> Incentives and social interactions</td></tr><tr><td>[227]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Display</td><td>W and Wh</td><td> Incentives and social interactions</td></tr><tr><td>[228]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Display and web</td><td>UNSPEC</td><td> Incentives</td></tr><tr><td>[29]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Display</td><td>Currency</td><td>GAMIFICATION, goal setting, recommendations and social interactions</td></tr><tr><td>[229]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative, normative and peer</td><td>Mobile</td><td>Currency, gCO2, metaphors and W</td><td> GAMIFICATION, goal setting, recommendations and social interactions</td></tr><tr><td>[230]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical, indicative and normative</td><td>Web</td><td>Currency, W and Wh</td><td> recommendations and social interactions</td></tr><tr><td>[231]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and peer</td><td>Display and mobile</td><td>Display</td><td>GAMIFICATION, goal setting, recommendations and social interactions</td></tr><tr><td>[232]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative and normative</td><td>Mobile and web</td><td>Mobile and web</td><td> recommendations, social interactions</td></tr><tr><td>[233]</td><td>End user and auto</td><td>Direct</td><td>Immediate</td><td>Desegregated, historical and indicative</td><td>Mobile</td><td>Wh</td><td> GAMIFICATION, goal setting, notifications, recommendations, social interactions and tips</td></tr><tr><td>[234]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Ambient, desegregated, historical, indicative and injunctive</td><td>Mobile</td><td>A, Humidity, PF, PPM, Temp, VA, VAR, W and Wh</td><td>Actuators and notifications</td></tr></tbody></table>
<table><caption>Table 19: Feedback system classification ICT</caption><thead><tr><th>Publication</th><th>Actor</th><th>Type</th><th>Frequency</th><th>Method(s)</th><th>Medium</th><th>Metrics & metaphors</th><th>Misc. features</th></tr></thead><tbody><tr><td>[235]</td><td>End user and expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>Ah</td><td>None</td></tr><tr><td>[236]</td><td>Decision maker and expert</td><td>Direct</td><td>Report</td><td>Indicative</td><td>Software</td><td>J</td><td>None</td></tr><tr><td>[237]</td><td>Decision maker and expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[238]</td><td>End user and expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>J</td><td>None</td></tr><tr><td>[501]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Mobile and web</td><td>Am, V and W</td><td>None</td></tr><tr><td>[391]</td><td>Auto and decision maker</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Software and web</td><td>Temp and W</td><td>None</td></tr><tr><td>[239]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Historical, indicative and injunctive</td><td>Software</td><td>A and W</td><td>None</td></tr><tr><td>[401]</td><td>Auto and expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>J and metaphors</td><td>None</td></tr><tr><td>[240]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Display</td><td>A, J and W</td><td>None</td></tr><tr><td>[431]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Historical and indicative</td><td>Web</td><td>W</td><td>None</td></tr><tr><td>[241]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>UNSPEC</td><td>None</td></tr><tr><td>[242]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>J</td><td>None</td></tr><tr><td>[38]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>Currency, gCO2 and W</td><td>None</td></tr><tr><td>[42]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>J and W</td><td>None</td></tr><tr><td>[441]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>None</td><td>None</td></tr><tr><td>[243]</td><td>End user and expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[41]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Mobile</td><td>W</td><td>None</td></tr><tr><td>[244]</td><td>End user and expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>J</td><td>None</td></tr><tr><td>[245]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[246]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated, indicative and injunctive</td><td>Software</td><td>J</td><td>None</td></tr><tr><td>[247]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W and J</td><td>None</td></tr><tr><td>[248]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W and J</td><td>None</td></tr><tr><td>[249]</td><td>End user and expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W and J</td><td>None</td></tr><tr><td>[250]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>None</td><td>None</td></tr><tr><td>[251]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>J</td><td>None</td></tr><tr><td>[252]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[253]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[51]</td><td>Auto, decision maker and expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[254]</td><td>Auto, decision maker and expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[255]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>A, V and W</td><td>None</td></tr><tr><td>[256]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[257]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Web</td><td>W and Wh</td><td>None</td></tr><tr><td>[258]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>J and W</td><td>None</td></tr><tr><td>[259]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>J and W</td><td>None</td></tr><tr><td>[260]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Web</td><td>A</td><td>None</td></tr><tr><td>[351]</td><td>Expert</td><td>Direct</td><td>Immediate</td><td>Desegregated and indicative</td><td>Software</td><td>W</td><td>None</td></tr><tr><td>[52]</td><td>End user</td><td>Direct</td><td>Immediate</td><td>Indicative</td><td>Software</td><td>Wh</td><td>None</td></tr></tbody></table>