# A systematic mapping study on software approaches analyzing human behavior in energy efficiency

Thomas Zaragoza, Adel Noureddine, Ernesto Exposito

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Thomas Zaragoza, Adel Noureddine, Ernesto Exposito. A systematic mapping study on software approaches analyzing human behavior in energy efficiency. Energy Research & Social Science, 2025, 122, pp.103991. 10.1016/j.erss.2025.103991. hal-04948860

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Submitted on 11 Jun 2025

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Review

# A systematic mapping study on software approaches analyzing human behavior in energy efficiency

Thomas Zaragoza *, Adel Noureddine *, Ernesto Exposito

University de Pau et des Pays de l'Adour, E2S UPPA, LIUPPA, Pau, France

ARTICLE INFO

Keywords:

Surveys and overviews

Feedback

Green IT

Energy

A B S T R A C T

In light of escalating carbon emissions and the increasing pressures on energy production, we believe it is essential to examine both: 1. The role of Information and Communication Technology (ICT) as either an enabler or a barrier to sustainability. 2. The role of human behavior, ranging from a contributing factor to energy consumption to a critical element in achieving energy optimizations. To address these factors, we conducted a systematic mapping study on software-based strategies for optimizing energy consumption, with a particular focus on incorporating human involvement in the process. Our findings indicate that ICT often serves as a facilitator of sustainability, especially in residential and commercial environments. However, the role of human behavior is more nuanced. By examining the energy optimization process as a loop consisting of four phases—monitoring, analysis, planning, and action—we found that human involvement spans multiple stages of this loop. While direct intervention in human behavior is commonly accepted and effective in residential contexts, this approach does not translate as directly to ICT, which tends to prioritize automated optimization. The aim of this paper is to raise awareness about these dynamics, explore how ICT is currently utilized to optimize energy use in residential settings, and suggest ways to adapt these approaches to make ICT itself more sustainable.

1. Introduction & context

In the last decade, there has been a significant growth in the Infor-
mation and Communications Technology (ICT) sector. There is an esti-
mated growth from 1.84 connected devices per person in 2010 (12.5
billion) to 6.58 per person in 2020 (50 billion), representing a 257.6 %
increase [1]. A tendency corroborated by another report that estimates
the number of mobile devices per user to grow from 2.10 (14.913
billion) in 2021 to 2.43 (18.221 billion) by 2025 [2]. Information
Technology (IT) is usually seen as a significant actor in promoting sus-
tainability and helping to mitigate the impact of climate change. In this
context, IT is described as *IT for Green*. *IT for Green* includes a variety of
strategies, technologies, and tools, such as:

* Implementing smart grids to optimize energy consumption and reduce waste.

* Leveraging data analytics to identify and address inefficiencies, reduce waste, and optimize energy usage.

* Using IoT sensors to monitor and optimize energy usage in buildings, transportation, and industrial processes.

* Digitizing paper-based processes, businesses can reduce their paper consumption, which can help reduce deforestation and waste.

* Using video conferencing tools and other virtual communication technologies, businesses can reduce the need for employees to travel for work, which can help reduce greenhouse gas emissions from transportation.

This increasing reliance on the ICT sector is not without cost. This
growth is accompanied by an increase in ICT Greenhouse Gas Emissions
(GHGE). It is estimated that, if left unchecked, ICT GHGE could grow
from approximately 1.6 % in 2007 to encompass around 14 % of
worldwide GHGE by 2040, relative to 2016 levels [3]. ICT is a broad and
dynamic industry that encompasses a range of technologies, applica-
tions, and services. The sector is typically made up of companies that
provide hardware, software, and services related to computing, tele-
communications, and the internet. The electricity consumption of the
entire computation sector, including—including data centers, personal
computing, and telecommunications—was approximately 2400 TWh (2.4
billion MWh) in recent years. This accounted for 2–3 % of global elec-
tricity consumption. Of this, 10–15 % can be attributed to personal

<figure><img src="image_2.png" /></figure>

<footnote>* Corresponding author.
E-mail addresses: thomas.zaragoza@univ-pau.fr (T. Zaragoza), adel.noureddine@univ-pau.fr (A. Noureddine), ernesto.exposito@univ-pau.fr (E. Exposito).</footnote>
computing devices such as PCs, laptops, and mobile phones, which includes energy-intensive accessories like monitors, and internet infrastructure [4] [5]. While the absolute energy consumption has increased due to the proliferation of digital devices and services, the relative share of energy use has decreased compared to 2010 (1078 million MWh or 6 % of the global electric consumption [6]), due to significant improvements in hardware efficiency and the migration to more efficient cloud-based systems. However, this progress does not negate the need for further reductions in energy consumption through more sustainable software practices. Increasing demand for computationally intensive applications, such as AI and video streaming, continues to exert pressure on energy resources. Thus, optimizing software to be more energy-efficient remains critical for curbing energy use in personal computing.

Green for IT (Green Information Technology) refers to the practice of designing, developing, using, and disposing of information responsibly to reduce its environmental impact. It involves reducing the environmental impact of IT by minimizing energy consumption, reducing carbon emissions, and cutting down on waste. Green for IT initiatives can take many forms, such as using energy-efficient hardware, implementing virtualization technology to reduce the number of physical servers, adopting cloud computing to optimize resource usage, and embracing sustainable IT practices like e-waste recycling. We believe that, while energy efficient- software and hardware development is promising, results can be limited, in part, by the rebound effect caused by user behavior. The rebound effect refers to when improvements in energy efficiency unintentionally lead to increased energy consumption. Indeed, human behavior can significantly influence the energy consumption of software. For example, factors like leaving devices running unnecessarily, using high display brightness or volume settings, and running multiple applications simultaneously can increase the demand on device resources and, consequently, energy usage. Driving behavioral change to reduce energy consumption has been a viable approach in the residential sector for decades, as demonstrated by Robert Kohlenberg et al. in 1976. They used a combination of injunctive feedback—a light bulb indicating when energy consumption exceeded 90 % of peak levels—and financial incentives, covering a portion of the electricity bill proportional to reductions in peak consumption. While energy use returned to baseline after the intervention, they achieved peak reductions of about 50 % [7]. With the democratisation and commercialisation of Internet of Things (IoT) in the household in the past two decades, energy consumption and human behavior can be better monitored and allowing for more complex, intelligent and efficient forms of feedback. And while studying human behavior has proved its potential in reducing occupant energy consumption, we believe these approaches have been sparsely adapted and used to address energy consumption in ICT.

It is within this context that we conducted a systematic mapping study to explore the relationship between human behavior and energy consumption as well as the role of ICT as both an agent and recipient of sustainability. In this paper we will contrast approaches in the building sector (residential & industrial) with ICT, study and synthesize the mechanisms and tactics used in the former, and suggest approaches to steer researches in the latter. Additionally, due to the importance of the human element in these approaches and the complexity of conducted experiences that include human candidates we will study how thoroughly proposed solutions are proved according to state of the art empirical standards to highlight potential weakness and areas for improvement.

The structure of this paper is as follows: Section 2 outlines our methodology, detailing the research questions, search process, inclusion criteria, and data extraction methods. In Section 3, we will present the results, which will be further analyzed and discussed in Section 4. Section 5 will address the limitations and threats to validity in our study. Finally, in Section 6, we offer recommendations for future research in ICT and sustainability, and we conclude with a summary in Section 7.

## 2. Study methodology

We have conducted this systematic mapping study according to the guidelines of Petersen et al. [8] [9] for conducting this type of study 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 abstracts 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 in Fig. 1. Notably, we did not perform the keywording of abstracts, which Petersen describes as a process to facilitate the creation of a classification scheme that incorporates existing studies.

### 2.1. Research questions

As the aim of this paper is to study the link between human behavior, energy consumption and the role software plays as both an agent and patient of sustainability, we seek to answer the following questions:

#### 2.1.1. RQ1: How do software solutions for energy management account for human behavior in energy consumption and optimization?

This research question aims to investigate two key aspects: first, how software solutions for energy management incorporate human behavior, either as a variable influencing energy consumption or as a target for optimization. Software solutions are understood as systems that integrate sensors (either physical or virtual) for data collection, potentially augmented by analysis algorithms, front-end visualizations for user feedback, or actuators for automation. Second, it explores how human behavior is represented in the academic literature, examining whether it is treated as a factor that affects energy consumption or as an active focus for intervention and optimization. By analyzing publication trends, this research seeks to understand the academic community's approach to human behavior in energy management and its implications for the design and effectiveness of software- based solutions. Ultimately, the goal is to identify opportunities for improving the integration of human behavior into future energy management systems and to guide sustainable computing practices.

#### 2.1.2. RQ2: What are the characteristics of these solutions to achieve energy optimization?

Depending on a multitude of variables ranging from the problem context to the method of optimization, the tailored approach will vary to suit the diverse needs. For example, an autonomous approach may not require any front-end or persuasive features to reduce energy consumption but may require a more in-depth network of sensors, actuators, and analysis algorithms to act efficiently, while a feedback system that depends on the user to intervene may require less back-end but necessitate a more polished user interface for providing feedback. The aim of this research question is to identify the contrasting needs and requirements of approaches according to the field of application and provide a synthesis of tools that can be implemented when targeting energy consumption.

#### 2.1.3. RQ3: How comprehensively are these solutions tested?

Our interest extends beyond the outcomes reported by the reviewed publications to include the nature and quality of the experiments conducted. Since accounting for human behavior is inherently complex,
<figure><img src="image_3.png" /><figcaption>Fig. 1. Flowchart of the search process.</figcaption></figure>

studies involving human participants must adhere to a rigorous set of conditions to ensure the data collected is reliable and can lead to empirically sound conclusions. The goal of this research question, particularly in studies involving human intervention, is to highlight the differences in experimental rigor across various fields of application and to identify any potential gaps or shortcomings in the methodologies used.

## 2.2. Search process

As recommended by Petersen et al. in their latest guidelines for conducting a systematic mapping study in software engineering [9], we used PICO (Population, Intervention, Comparison, and Outcomes) to identify keywords and formulate search strings from the research questions. In our study, we define the population as “studies discussing software for managing energy consumption” that intervene on “human behavior and user habits”. Based on the previous definitions and some fine-tuning, we produced for the SCOPUS database the query seen on Fig. 2. We refer to managing energy consumption as the process of optimizing the use of energy as a resource, aiming to improve efficiency by reducing waste and increasing the output-to-consumption ratio. This involves using strategies or technologies that ensure energy is used in a way that maximizes productivity while minimizing unnecessary consumption. Essentially, it focuses on achieving more effective energy use, where less energy is needed to produce the same or greater results, enhancing overall energy efficiency.

While behavior and behavioral are naturally derived from the topic of this study, we found the term habit, which, while less common, is still significantly used interchangeably with behavior. The same reasoning can be applied to energy and human, which were the terms naturally derived from the research questions, and which we were then able to expand with power & green and user & consumer, respectively, through fine-tuning of our initial request and the analysis of pertinent publications that did not match the initial parameters.

We then translated and adapted this query to fit the syntax and limitations of each database. Scopus, IEEE, Science Direct, and Web of Science did not discriminate search strings based on whether or not American or British English was employed, but ACM DL did, requiring both ‘behavior’ and ‘behavioral’ to also be written as ‘behaviour’ and ‘behavioural’. Additionally, neither IEEE, ACM DL, nor Web of Science had a field search that allowed us to search for entries containing our search strings simultaneously in their titles, abstracts, or keywords thus requiring us to split the query for each field. We were limited by Science Direct, as it would not allow us to use more than eight Boolean connectors, which we circumvented by intersecting the results of multiple sub- requests. Finally, we were able to limit results to English and computer science only on Web of Science, Science Direct and Scopus.

The search took place in September 2024. We found that, due to a quirk in the ACM Digital Library, we had a discrepancy between the number of entries exported and the number of results ACM returned. This discrepancy was due to duplication. After deleting these duplicates,

the results of this search can be visualized in Table 1, which presents the amount of entries found in the five databases queried: IEEE, ACM DL, Science Direct, Scopus, and Web of Science. We regrouped every publication and proceeded to automatically prune duplicates with identical titles. After pruning, we had a total of 5550 studies left, which we then had to manually filter according to the inclusion and exclusion criteria we defined.

The inclusion criteria were elaborated to reflect our research questions.

### 2.2.1. IC1: the publication's main contribution is software related

We do not restrict this study to a specific field of application; however, we do limit it to a specific field of publications. The main contribution should be the proposition or development of a system architecture and/or artifact.

### 2.2.2. IC2: the publication takes into consideration human behavior

The user's behavior must be taken into consideration in the main contribution of the publication. This can range from simply studying the user's interactions with a system, such as clicks per minute (CPM), to complex cyber-physical systems (CPS) equipped with a variety of monitors and sensors to study the user in a physical setting equipped with AI technology.

### 2.2.3. IC3: the publication is related to energy consumption and management

The objective of the publication should be to optimize energy management. Energy optimization involves either reducing energy usage through measures such as hardware improvements and behavior changes or maintaining the same energy consumption while minimizing environmental impact, for example, by improving grid efficiency or reducing peak demand to lower the carbon footprint of energy providers.

### 2.2.4. IC4: the publication is peer reviewed

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

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

<table><caption>**Table 1**
Distribution of papers found per source.</caption><thead><tr><td>Source</td><td>#Publications</td></tr></thead><tbody><tr><td>IEEE</td><td>538</td></tr><tr><td>ACM DL</td><td>3211</td></tr><tr><td>Science Direct</td><td>454</td></tr><tr><td>Scopus</td><td>1144</td></tr><tr><td>Web of Science</td><td>203</td></tr><tr><td>Total</td><td>5550</td></tr></tbody></table>

TITLE-ABS-KEY((Behavior OR Behavioral OR Habit) AND (Energy OR Power OR Green) AND (Software) AND (Human OR User OR Consumer)) AND (LIMIT-TO (SUBJAREA, "COMP")) AND (LIMIT-TO (LANGUAGE, "English"))

<caption>Fig. 2. Scopus search query.</caption>
### 2.2.5. 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 could limit the results of publications to English in the following databases: *Web of Science*, *Scopus*, and *Science Direct* we had to manually exclude non-English publications from *ACM Digital Library* and *IEEE Xplore*.

### 2.2.6. EC2: the publication is superseded by subsequent research

We chose to exclude preliminary publications by the same primary authors on the same project. If two publications by the same authors present similar contributions and meet the inclusion criteria, we included only the most recent publication that reflects the latest research developments. We excluded two publications based on these criteria.

### 2.2.7. EC3: the publication is not publicly accessible or through our academic resources

In the case that the full text of a publication satisfied the inclusion criteria and could not be accessed publicly or through our resources, we contacted the authors directly to request their paper. If we do not receive a response we exclude the paper. We had to exclude one publication due to this criterion.

We filtered the remaining 5550 publications according to the inclusion criteria by reading the titles, abstracts, and the publication more thoroughly if the abstract alone was not sufficient to achieve a common consensus. As a result of this filtering, we were able to drastically reduce the number of publications in this study from 5550 to 55.

### 2.3. Data extraction & snowballing

For data extraction, we used the template provided by Petersen et al. [9] and represent the data items and values we wish to extract in Table 2 with their corresponding research questions.

To explore the complex relationship between energy and human behavior in software in RQ1, we start with a meta-analysis of publications to outline general trends. We study venue types, names, publishing dates and author keywords. Venue types are categorized 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. We then provide a deeper analysis of papers by determining the field of analysis and decomposing the solutions proposed by authors as autonomic models and drawing correlations between the two. The application field was

<caption>**Table 2**</caption>

Data extraction form used in this study, fields labeled with an asterisk were extracted automatically.

<table><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 Date*</td><td>Year of publication</td><td>RQ1</td></tr><tr><td>Author Keywords*</td><td>List of keywords selected by authors</td><td></td></tr><tr><td>Application Field</td><td>Field meant to benefit from the publication</td><td></td></tr><tr><td>Management Method</td><td>Automatic and/or human</td><td></td></tr><tr><td>Feedback narratives</td><td>None, historical, etc.</td><td></td></tr><tr><td>Metrics & metaphors</td><td>Wh, CO2, etc.</td><td>RQ2</td></tr><tr><td>Misc. features</td><td>None, gamification, etc.</td><td></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 and Qualitative data sorted into categories</td><td>RQ3</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 amount of participants</td><td></td></tr></tbody></table>

determined manually by reading the publications. We have found that papers in the corpus are only classified into one of two distinct fields of application: *Building* and *ICT*, with the latter being further separated into *Software* and *Hardware*. The building category regroups all publications that aim to increase electricity consumption efficiency in the residential/commercial sector. The ICT category groups all publications that aim to reduce ICT-related energy consumption, which are split into two distinct classes: with the software category grouping publications that aim to reduce consumption on a software level through code optimization or user behavioral change, and hardware grouping publications that aim to reduce consumption on a hardware level, ranging from smarter idle times to component throttling.

Each paper's solution is split into the 4 autonomic sub-elements: Monitoring, analyzing, planning, and executing [10]. For each paper in this study, we have analyzed the four autonomic sub-elements of their systems and classified them as either human, hybrid, or automatic, which we define as follows:

* Human, for when the autonomic sub-element depends fully on human intervention. For example, a mobile game that tasks participants to cooperate to reduce consumption during game hours. While monitoring is automated, the planning and execution of intervention depends fully on its human players [11].

* Automatic, when the autonomic sub-element is fully independent of human intervention. For example, a web ontology language-based approach that, by studying occupancy behavior, can result in automatic adaptations for reduced energy consumption in case of conflicting events: if the occupant is sleeping, there is no way the occupant can also be watching the television, so it is turned off [12].

* Hybrid, for when the autonomic sub-element either depends on cooperation between human and the system or when the system offers both options independently. This is the type employed in recommendation tools for appliance load-shifting [13].

To summarize, we adopted a dual approach. First, we conducted a meta-analysis of publication counts, venue types, publishing dates, and author keywords to identify trends and the overall popularity of incorporating human behavior in energy optimizations over time. This analysis provided a high-level overview of how human behavior is integrated across different studies, bringing attention to key aspects such as the growing popularity of works that account for human behavior, the maturity level of the field, and the development of specialized venues dedicated to these topics. Additionally, it highlighted gaps in the field, such as the lack of venues specifically focused on human behavior in energy optimization, providing insights into how the academic landscape has evolved to address this crucial factor. Next, we conducted a deeper analysis by categorizing papers based on their application field (Building vs. ICT) and management methods. By cross-referencing these categories, we were able to examine the varying role of humans in energy optimization, depending on the context, ranging from a mere variable to the target of intervention, thereby offering further insights.

To answer RQ2, we focus on user-centric solutions and analyze their characteristics. We will be dissecting the proposed solutions according to the feedback taxonomy suggested in an ulterior publication [14]. We dissect feedback according to several key elements, beginning with feedback narratives, data visualization (graphs and metrics), and miscellaneous features. Feedback narratives refer to how the data is structured to convey a 'story' to users, aiming to create a coherent and meaningful connection between their energy usage and the desired intervention which we list as follows:

* Historical feedback is characterized by allowing users to compare their consumption over different periods of time [15–18].

* Disaggregation feedback is characterized by allowing users to compare their consumption across different depths of granularity. In
the context of smart homes, this would involve visualizing energy consumption per appliance and/or room [19–23].

* Normative feedback is characterized by allowing users to compare their consumption with users of a similar profile, such as allowing residents to compare their energy consumption to that of other similar households [24].

* Peer feedback is similar to the previous feedback but with a focus on the social aspect, prioritizing the comparison between users who know each other and can discuss the subject over a similar profile.

* Injunctive feedback provides an objective for the user to achieve, rather than solely relying on the user's initiative to set their own goals [25].

The effectiveness of certain graphs and metrics can vary depending on the feedback narrative and the user's profile or preferences. Additionally, miscellaneous features can be implemented to enhance the feedback's ability to inform, persuade, and drive user intervention. These features, which include but are not limited to, are as follows:

* Gamification refers to the application of game-like features—such as scores, points, rewards, leaderboards, and shops—to non-game contexts in order to enhance user engagement and motivation. By tapping into competitive or reward-based incentives, gamification encourages users to perform desired actions or behaviors in a more enjoyable and engaging way. In the serious game Energy Battle, users monitored energy consumption and accessed feedback online. They received tips for reducing usage, earned rankings based on team performance, and gained fictitious currency to create block-based artwork. However, the game faced mechanical issues, such as the limited availability of initial blocks and the difficulty of obtaining more, highlighting the challenge of balancing game mechanics. [26].

* Social interaction leverages the pressures for conformity and the intrinsic satisfaction gained from a sense of community to enhance user engagement and motivation [27]. For example, the design of a website to provide users feedback on their energy consumption, with the added feature of allowing users to compare their consumption rates to other users. This has the benefit of sparking social interaction between users about their consumption and can provide motivation for environmentally responsible behavior.

* Incentives & disincentives consist of providing material or token incentives (or disincentives) to drive behavioral change. For example, we can see incentives implemented in the papers of *Rimas Gulbinas* et al. and *Hamid Abdi* et al. In the latter publication, the authors propose a system of distributed displays in an office building to provide employees with raw and direct feedback on their individual energy consumption which will reward employees with the financial incentive from a fraction of the cost saved from achieved energy reductions [28].

To summarize, we focus on analyzing key elements such as feedback narratives, feedback features, and metrics & metaphors to examine software solutions that drive energy optimization through behavioral change. This approach provides a practical framework for designing future solutions by highlighting the diverse ways in which feedback can be structured and utilized. Additionally, we cross-reference this analysis with insights from RQ1 to explore how solution characteristics differ across application fields (e.g., building vs. ICT) and uncover actionable lessons from these contrasts.

Conducting field experiments, surveys, and interviews is crucial when studying user behavior, as these methods provide real-world insights into how people interact with systems or products. For this reason, we will be extracting experiment types, duration, characteristics, as well as the presence of surveys and interviews to answer RQ3. Field experiments allow researchers to observe behavior in natural settings, offering more accurate data than laboratory settings, where behavior may be influenced by artificial conditions.

* Type of experiment: Simulation (experiment is conducted on simulated users), controlled (experiment takes place in a controlled situation/lab), and field experiment (experiment is conducted outside of a controlled situation).

* Duration of experiment, which we have split into three categories: pre-intervention, intervention, and post-experiment. Pre-intervention corresponds to the monitoring and measuring of a baseline before the intervention while post-intervention corresponds to the monitoring period after the end of the intervention to study the lasting effects.

* Number of participants.

* Control group: whether or not the conducted experiment had a control group.

The presence of control groups is necessary to limit the influence of extraneous variables, such as an unusually warm winter that could reduce heating needs in an experiment on lowering HVAC consumption. Similarly, increasing the number and diversity of participants enhances the statistical power of the findings, allowing for more accurate generalization. Surveys gather broad perspectives on user preferences, habits, and motivations, while interviews provide deeper, qualitative insights, allowing researchers to understand the reasons behind users' actions and perceptions. Surveys conducted were manually extracted and classified according to definitions proposed by Colin Robinson and Kieran McCartan [29]. The surveys are either questionnaires, focus groups, or interviews and the data is quantitative and/or qualitative. We have extracted the number of participants, the type of data produced, as well as the nature of the data produced from each paper to facilitate both comparison between publications and answering the research questions. We propose the following classification scheme to describe the nature of the data produced from these surveys:

* Demographic: information relevant to the participant's age, political views, gender, etc.

* Design: the participant's feedback on how to alter the proposed solution. (e.g., aesthetics, method, period, etc.)

* Behavior: to study the perceived level of effectiveness of the proposed solution in achieving its desired outcome of reducing energy consumption through behavioral change.

* Habits: information relevant to participant's consumption habits. (e. g., number of devices, time spent on PC, etc.)

* Satisfaction: to measure the participant's satisfaction with the proposed solution.

* Usage: to gauge the participant's level of interaction with the proposed solution.

Combining these methods ensures a comprehensive understanding of user behavior, which is crucial for designing effective, user-centered solutions. Additionally, we determined whether or not there is post-intervention monitoring to study the lasting effects.

To summarize, we extracted and analyzed key aspects of the methodologies employed in the reviewed studies. By categorizing experiments into simulations, controlled settings, and field experiments, we highlighted the extent to which solutions are tested in realistic environments versus artificial conditions. The inclusion of pre-intervention, intervention, and post-experiment phases in our analysis allowed us to evaluate whether studies capture the full lifecycle of user behavior and intervention impacts, including long-term effects. We also assessed the presence of control groups to ensure the validity of findings and the number and diversity of participants to gauge statistical power and generalization. Furthermore, we examined the use of surveys and interviews to understand how user behavior is assessed, including the collection of quantitative and qualitative data on demographics, habits, satisfaction, and feedback. This systematic approach provides a comprehensive view of the rigor and completeness of testing methodologies across the studies, enabling us to identify gaps and strengths in
the evaluation of energy optimization solutions. From the remaining 28 publications, we performed backward snowballing –a process detailed by Samireh Jalali and Claes Wohlin [30] – which involves examining the reference lists of these papers to identify additional relevant studies. Relevant studies were then assessed against our inclusion and exclusion criteria, ensuring they met the required standards, resulting in the addition of 27 publications for a total of 55 publications. With the research questions elaborated, the publications chosen, and the data extracted, we can begin the analysis of the aforementioned data to provide answers to our questions.

## 3. Results

The analysis section is decomposed into three different subsections according to each research question elaborated starting with the meta-analysis of trends and keywords of RQ1.

### 3.1. RQ1: how do software solutions for energy management account for human behavior in energy consumption and optimization?

To answer our first research question, we can start by analyzing the publication dates and venue types of the publications included in our study, represented in Fig. 3. Among the total of 55 publications, a majority of 32 (58.2 %) publications originates from conference proceedings showing room for the production of more mature works. In Fig. 3 we can see there seemed to be a trend mid 2010's that has died down the last decade despite the increasingly important energy crisis. In Table 3 we can visualize the name and types of venues, from which, at least 2 publications in this study corpus were published in. Of the 55 publications, 44 (80,0 %) studies were published uniquely in different venues which suggests a lack of prominent multidisciplinary venue for receiving computer science papers that study human behavior in energy management contexts. The 11 remaining studies are distributed between *Smart Cities and Green ICT Systems (SMARTGREENS)*, *Environment and Electrical Engineering (EEEIC)*, *Energy and Building, Energy and Applied Energy* which, unsurprisingly, all share the same talking point of energy efficiency with *SMARTGREENS* and *Energy and Buildings* also sharing the added specificity of targeting the energy efficiency of buildings.

#### 3.1.1. Author keyword analysis

To better exploit the author keywords and offer a more detailed analysis of existing trends we created our own classification according to the primary axis tackled in this study (Energy, human behavior and software) as well as eco-feedback. From these four major axes, we created 10 different classes:

1. Behavioral monitoring, which groups keywords that relate to the study and/or prediction of user habits.

Publication count by venue type and year

<figure><img src="image_4.png" /><figcaption>Fig. 3. Amount of publications per year and per venue type.</figcaption></figure>

<table><caption>**Table 3**

Publications per venue (over 1 publication).</caption><thead><tr><td>Venue name</td><td>Venue type</td><td>Total</td></tr></thead><tbody><tr><td>Conference on Smart Cities and Green ICT Systems (SMARTGREENS)</td><td>Proceedings</td><td>3</td></tr><tr><td>Conference on Environment and Electrical Engineering (EEEIC)</td><td>Proceedings</td><td>2</td></tr><tr><td>Energy and Buildings</td><td>Journal</td><td>3</td></tr><tr><td>Applied Energy</td><td>Journal</td><td>2</td></tr><tr><td>Energy</td><td>Journal</td><td>2</td></tr></tbody></table>

2. Behavioral change, which groups keywords that relate to the study and/or driving of behavioral change of users.

3. Energy monitoring, which groups keywords that relate to the measurement of energy consumption.

4. Energy reduction, which groups keywords that relate to or participate in reducing energy consumption and improving energy efficiency.

5. Green for IT: Software, which groups keywords that relate to energy optimization of IT at the software level.

6. Green for IT: Hardware, which groups keywords that relate to energy optimization of IT at the hardware level.

7. IT for Green, which groups keywords that relate to the application of IT solutions for sustainability in other fields.

8. Feedback, which groups keywords that relate to the visualization of energy data for users.

9. Gamification, which groups keywords that relate to the use of gamification. Gamification is the use of game mechanisms like achievements, badges, leader boards etc. with the aim of promoting environmentally responsible behavior.

10. Social interaction, which groups keywords that relate to the use of social norms and social networking. Social interaction consists of using social interactions and cues in a community to encourage behavior change.

Classifying author keywords allows us to conglomerate infrequent keywords into study-able trends. Author keywords were classified according to their definitions and the context in which they were employed and the result of this classification can be visualized in Table 6. For example, the author keyword *motivation* was classified under the category **behavioral change**, as motivation was provided to encourage environmentally responsible behavior and thus drive behavioral change. When all author keywords were classified, we counted the number of publications per class and represented it on Table 5. These classes are not disjoint; a publication can be assigned to multiple classes according to its author keywords.

Of the 55 publications, only 46 possess author keywords. As expected, a majority of 40 (86.9 %) publications contain energy reduction related keywords but if we study the other author keyword classes we can gain valuable insight into the trends of two facets. Firstly, we can see a trend of IT and software being used as a tool for achieving energy optimization rather than being the target of energy efficient interventions: 18 (39.1 %) publications contains keywords about *IT for green* compared to the 9 (19.5 %) of publications that contains keywords about *green for IT*. Even when IT is the target of energy optimization, there is a clear trend favoring the improvement of hardware components and systems as a whole rather than software and its usage with only 2 (4.3 %) publications possessing author keywords that relate to optimization of software compared to the 8 (17.4 %) of hardware.

Secondly, on a human behavior level, only 19 (41.3 %) publications contain keywords that refer to the monitoring of human behavior for energy optimization compared to the 11 (23.9 %) for driving behavioral change which shows a comparatively weak trend for driving behavioral change even when human behavior is considered.

Lastly, on an eco-feedback level, we can see a noticeable trend of the use of persuasive technologies with 19 (41.3 %) publications containing
feedback-related keywords, 7 (15.2 %) publications containing gamification related keywords and 4 (8.7 %) publications containing social interaction related keywords. From this, we can infer the importance these publications place on including users in energy management.

### 3.1.2. Application field

While studying keywords alone might be enough to get surface level appreciation of trends, to more thoroughly assess the existing approaches for studying human behavior in regards to software energy consumption we have read each paper to determine the intended application field by authors, the results of which can be visualized in Table 4. The classification in these figures is disjointed. A majority of publications aimed to optimize energy consumption in residential or office settings while accounting for occupant behavior. Of the 54 total publications, 41 (75.9 %) aims to directly intervene in building energy efficiency by either load-shifting energy intensive appliances (For example, the publications by *Dustin Wright* et al. [31] and *Seán Óg Murphy* et al. [36] that aim to forecast energy-intensive activities and recommend more cost and energy efficient schedules for users through their mobile devices.) or reducing energy consumption altogether through behavioral change or optimisations. This, in part, explains the strong trend of keywords for *eco-feedback* and *IT for green* in Table 5 as of the 41 publications that target building energy efficiency, a majority of 33 (80.5 %) attempt to do so by reducing energy consumption through occupancy behavior. They commonly employ persuasive technologies and forms of eco-feedback.

When it comes to tackling energy efficiency in ICT, of the 13 publications, 9 (69.2 %) do so through the optimization of hardware (smarter idle times or throttling hardware components based on user habits). For example, in the publication of *Chaib Draa* et al. [52] they automatically throttle the audio and brightness components to achieve energy consumption reductions and, in a later paper [35], propose to automatically throttle Wi-Fi, CPU and GPS components according to user needs on top of adjusting screen brightness. This leaves the remaining 4 (30.8 %) publications to reduce software energy consumption through behavioral change or automatic adaptations. Of these 4 publications, the 2 publications of *Christian Bunse* [65] and *Adel Noureddine* et al. [45] reduce software energy consumption solely through behavioral change alone with the former studying mobile phone users and the latter studying PC users. In the publication of *Frederic Camps and Jean Fanchon* [46], they attempt to drive behavioral change through website recommendation and reduce energy consumption through automatic cache optimization. Finally, in the publication of *Angel Cañete* et al. [40] they achieve energy consumption reduction solely through automatic adaptations of software according to user habits and needs.

### 3.1.3. Autonomic sub-elements

Each paper was analyzed to determine the nature of each individual autonomic sub-elements, the result of which can be visualized in Tables 7 and 8, summarized in Table 9.

When it comes to monitoring, all papers in this study have an automated system in place to gather, transfer and store data. The contrast appears when we look at the analysis, we found that 20.4 % of publications relied on human analysis. We consider the analysis to be human when the data is presented raw with little to no transformation and analysis work. In the proposed solution of *Hamid Abdi* et al. for saving energy in commercial buildings, the energy consumption is automatically measured and displayed on monitors of the desks of

<table><caption>**Table 4**
Publications per target field of application.</caption><thead><tr><th rowspan="2">Building</th><th colspan="2">ICT</th></tr><tr><th>Software</th><th>Hardware</th></tr></thead><tbody><tr><td>39</td><td>4</td><td>9</td></tr></tbody></table>

<table><caption>**Table 5**
Amount of publications per author keyword classification.</caption><thead><tr><th>Category</th><th>Amount of publications</th></tr></thead><tbody><tr><td>Behavioral Monitoring</td><td>36</td></tr><tr><td>Behavioral Change</td><td>21</td></tr><tr><td>Energy Monitoring</td><td>31</td></tr><tr><td>Energy Reduction</td><td>78</td></tr><tr><td>Green for IT: Software</td><td>4</td></tr><tr><td>Green for IT: Hardware</td><td>16</td></tr><tr><td>IT for Green</td><td>34</td></tr><tr><td>Feedback</td><td>38</td></tr><tr><td>Gamification</td><td>14</td></tr><tr><td>Social Interaction</td><td>8</td></tr></tbody></table>

employees. The amount of watts consumed must be analyzed by the employee who may or may not have to decide whether or not they should adapt their behavior [33].

We found that 38.8 % of publications did not rely on any form of human analysis. For example, in the publication of *Angel Cañete* et al., their proposed solution analyze the context of a user on an android application, the status of the device and the application state to perform energy reduction adaptations leaving the user out of the loop. And we have found that 40.8 % of publications have a hybrid form of analysis. In another, they propose a serious game composed of multi-colored monsters that represent the well-being of the participant's household. Their solution analyses the energy consumption and reductions achieved and according to the results of this analysis the monsters are more or less healthy and better ranked compared to their peers. The participant still needs to play the game and understand the well-being and environment of their monster in order to plan their next actions. However, the raw data is initially processed, which is why we consider the analysis to be hybrid [56].

When it comes to planning, this process is more human-centric with 48.1 % of publications relying solely on human planification. In the publication by *P. Wesley Schultz* et al. they installed a physical display in the home of participants that displayed the current usage and cost with tri-color lights to signal if they are on above, on par or beneath past consumption, but to achieve energy reductions the users must develop a plan themselves. We found that 37.0 % of publications had an automated planification. This was typical in load-shifting solutions. For example, the analysis, generation of appliance usage to calculate an optimized appliance schedule to reduce the strain on the grid [61]. The number of hybrids in the planification phase is far fewer with only around 14.9 % of publications. In the publication of *Aniruddha Desai and Jugdutt Singh* they have designed an automated energy consumption monitoring data that analyses energy consumption, calculates predictions and proposes energy reduction plans to the user. Nonetheless the user still has access to the same data, to perform their own analysis and generate their own plan [44].

Finally when it comes to taking action, only 22.2 % of publications automated their actions. In their paper *Ismat Chaib Draa* et al. proposed an architecture that extracts user context information, analyses it, and autonomously throttles brightness and audio components to reduce energy consumption [52]. A small amount of publications (9.3 %) employ a hybrid approach to executing the planned actions. There are two different cases that fit this description, a publication that can automate the actions but require human authorization and a publication that can, at choice, automate the planning and execution or let the burden of planning and executing fall on the human actor. For the former we can look back to the example of *Seán Óg Murphy* et al. [61]. For the latter we can look to the publication of *Miguel Jimeno* et al. where inactivity on computers in a computer lab is studied and the information is either conveyed to an admin who can then send energy-saving commands to specific computers or can be done so autonomously [38]. A majority of publications (68.5 %) rely solely on human intervention to execute the plans. In their publication, *Anton Gustafsson* et al. designed a
<table><caption>**Table 6**
Author keyword classification.</caption><thead><tr><th>Behavioral monitoring</th><th>Behavioral change</th><th>Energy monitoring</th><th>Energy reduction</th><th>Green for IT: Software</th><th>Green for IT: Hardware</th><th>IT for Green</th><th>Feedback</th><th>Gamification</th><th>Social interaction</th></tr></thead><tbody><tr><td>Activity recognition</td><td>Behavioral intervention</td><td>Smart meters</td><td>Demand management</td><td>Green software</td><td>Mobile systems</td><td>Smart homes</td><td>Feedback</td><td>Serious games</td><td>Social norms</td></tr><tr><td>Activity prediction</td><td>Motivation</td><td>Smart metering</td><td>Recommendations</td><td>Green for IT: Software</td><td>Internetware</td><td>Household electricity</td><td>In-home displays</td><td>Virtual pet</td><td>Message frame</td></tr><tr><td>User profiling</td><td>Behavior</td><td>Hardware</td><td>Appliance control</td><td>Energy saving tips</td><td>Owl</td><td>Smart cities</td><td>Message frame</td><td>Pervasive games</td><td>Social frame</td></tr><tr><td>User behavior</td><td>change</td><td>Current measurement</td><td>Automation</td><td>Building</td><td>Software evolution</td><td>Cyber-physical systems (cps)</td><td>Energy feedback</td><td>Persuasive games</td><td>Incentives</td></tr><tr><td>Human behavior</td><td>support</td><td>Power</td><td>Energy savings</td><td>Electricity consumption</td><td>Software sustainability</td><td>Office</td><td>Visualization</td><td>Serious games</td><td>Engaging interaction</td></tr><tr><td>Human inactivity</td><td>Human inactivity</td><td>Reactive</td><td>Energy consumption</td><td>Energy</td><td>Computer</td><td>Smart home</td><td>Human computer interaction</td><td>Advergame</td><td>Social networking</td></tr><tr><td>User requirement</td><td>Behavior</td><td>power</td><td>Energy consumption</td><td>Household energy</td><td>Desktop</td><td>Mobile computing</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Reinforcement learning</td><td>Behavior</td><td>meter reading</td><td>consumption</td><td>Energy efficiency</td><td>computers</td><td>Organizational buildings</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Human-in-the-loop</td><td>Behavior</td><td>Advanced metering</td><td>Energy conservation</td><td>Energy</td><td>Embedded agent</td><td>Public building</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Context-awareness</td><td>Behavior</td><td>Smart monitoring</td><td>Energy</td><td>Sustainability</td><td>Mobile device</td><td>Residential house</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Context</td><td>Behavior</td><td>Energy</td><td>Energy</td><td>Energy</td><td>Smart-phones</td><td>Wireless sensor networks</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Behavior</td><td>Behavioral modification</td><td>monitoring</td><td>Unregulated energy</td><td>Energy use</td><td>Android</td><td>System architecture</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Device's context</td><td>Behavioral</td><td>Monitoring</td><td>Energy</td><td>Virtualization</td><td>Client-server architecture</td><td>Smart gateway</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Artificial intelligence for humans-in-the-loop</td><td>change</td><td>Monitoring</td><td>Energy</td><td>Electrical energy</td><td></td><td>Urban informatics</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Behavior modeling for humans-in-the-loop</td><td>change</td><td>Infrastructure</td><td>Load monitoring</td><td>Load shifting</td><td></td><td>Embedded computing</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Usage pattern</td><td>NILM</td><td>Sensors</td><td>Energy tariffs</td><td>Recommender systems</td><td></td><td>Internet of things</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Applications sequences prediction</td><td></td><td>Appliance power</td><td>Demand response</td><td>Energy saving</td><td></td><td>Mobile app distributed</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Deep learning</td><td></td><td>signatures</td><td>Home automation</td><td>Smart grid</td><td></td><td>software infrastructure</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Ambient intelligence</td><td></td><td></td><td>Energy reduction</td><td>OpenADR</td><td></td><td>Distributed systems</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Run-time user and application analysis</td><td></td><td></td><td>Energy reduction</td><td>Demand side management</td><td></td><td>Distribution</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Classification</td><td></td><td></td><td>Energy</td><td>Policy engine</td><td></td><td>Commercial buildings</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Machine learning</td><td></td><td></td><td>Energy</td><td>Environmental sustainability</td><td></td><td>Cloud computing</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td>Occupancy detection</td><td></td><td></td><td>Energy</td><td>Sustainable HCI</td><td></td><td>Residential energy management</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Energy policy</td><td>Energy-saving potential</td><td></td><td>Cyber-physical systems</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Energy waste</td><td>Energy policy</td><td></td><td>Mobile application</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Dynamic power management</td><td>Dynamic power management</td><td></td><td>Human-building interaction</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Energy saving policies</td><td>Energy saving policies</td><td></td><td>Green computing</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Actuators</td><td>Energy management</td><td></td><td>Service-oriented computing</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Energy</td><td>Time-of-use pricing</td><td></td><td>Mobile App</td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Management</td><td>Home energy management</td><td></td><td></td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Appliance scheduling</td><td>Power saving profiles</td><td></td><td></td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Power saving</td><td>Dynamic adaptation</td><td></td><td></td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Power</td><td>Power consumption</td><td></td><td></td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>Recommending</td><td>Recommending</td><td></td><td></td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr><tr><td></td><td></td><td></td><td>UWB Energy saving</td><td>UWB Energy saving</td><td></td><td></td><td>Advergame</td><td>Serious gaming</td><td>Social computing</td></tr></tbody></table>

serious game that gives tasks for players and their household to perform during game hours to reduce their energy consumption [11].

We believe that the more a user is invested in the energy reduction process the higher the chance their behavior will change. We can notice a trend of increasingly relying on human intervention the further we are

situated in the autonomous loop. A majority of papers include a human in the loop through analysis, planning and execution (respectively 61.1 %, 63.0 % and 77.8 %) but that still leaves a significant minority of publications that do not. There is an important contrast in human intervention if we compare the fields of application as seen on Table 9.
<table><caption>**Table 7**
Energy management method (Part 1).</caption><thead><tr><th>ID</th><th>Monitoring</th><th>Analysis</th><th>Planning</th><th>Action</th></tr></thead><tbody><tr><td>[31]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[32]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Human</td></tr><tr><td>[33]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[15]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[16]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[34]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[35]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[17]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[19]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[36]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Human</td></tr><tr><td>[37]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[38]</td><td>Automatic</td><td>Hybrid</td><td>Hybrid</td><td>Hybrid</td></tr><tr><td>[39]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Human</td></tr><tr><td>[40]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[12]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[18]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[41]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[42]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Human</td></tr><tr><td>[20]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[25]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[21]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[43]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[44]</td><td>Automatic</td><td>Hybrid</td><td>Hybrid</td><td>Hybrid</td></tr><tr><td>[45]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[46]</td><td>Automatic</td><td>Automatic</td><td>Hybrid</td><td>Hybrid</td></tr><tr><td>[47]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Hybrid</td></tr><tr><td>[13]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Hybrid</td></tr></tbody></table>

<table><caption>**Table 8**
Energy management method (Part 2).</caption><thead><tr><th>ID</th><th>Monitoring</th><th>Analysis</th><th>Planning</th><th>Action</th></tr></thead><tbody><tr><td>[48]</td><td>Automatic</td><td>Automatic</td><td>Hybrid</td><td>Human</td></tr><tr><td>[49]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[50]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Human</td></tr><tr><td>[51]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[52]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[53]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[26]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[23]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[11]</td><td>Automatic</td><td>Human</td><td>Automatic</td><td>Human</td></tr><tr><td>[24]</td><td>Automatic</td><td>Hybrid</td><td>Human</td><td>Human</td></tr><tr><td>[54]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[55]</td><td>Automatic</td><td>Human</td><td>Hybrid</td><td>Human</td></tr><tr><td>[56]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[57]</td><td>Automatic</td><td>Human</td><td>Hybrid</td><td>Human</td></tr><tr><td>[58]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[59]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[22]</td><td>Automatic</td><td>Automatic</td><td>Automatic</td><td>Automatic</td></tr><tr><td>[60]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[61]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[28]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[62]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[63]</td><td>Automatic</td><td>Human</td><td>Hybrid</td><td>Human</td></tr><tr><td>[64]</td><td>Automatic</td><td>Hybrid</td><td>Hybrid</td><td>Human</td></tr><tr><td>[65]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[66]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[67]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[68]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr><tr><td>[69]</td><td>Automatic</td><td>Human</td><td>Human</td><td>Human</td></tr></tbody></table>

<table><caption>**Table 9**
Publications per autonomic phase for the building sector.</caption><thead><tr><th rowspan="2">Planning phase</th><th colspan="2">Human</th><th colspan="2">Hybrid</th><th colspan="2">Automatic</th></tr><tr><th>Building</th><th>ICT</th><th>Building</th><th>ICT</th><th>Building</th><th>ICT</th></tr></thead><tbody><tr><td>Monitoring</td><td>0</td><td>0</td><td>0</td><td>0</td><td>41</td><td>14</td></tr><tr><td>Analysis</td><td>9</td><td>3</td><td>21</td><td>1</td><td>11</td><td>10</td></tr><tr><td>Planning</td><td>24</td><td>3</td><td>6</td><td>2</td><td>11</td><td>9</td></tr><tr><td>Action</td><td>35</td><td>3</td><td>3</td><td>2</td><td>3</td><td>9</td></tr></tbody></table>

Only 23.1 % of publications in the ICT field of application rely, in part, on humans for analysis and 30.8 % for planning and execution. In contrast, 73.2 % of publications in the building field of application rely on humans for analysis and planning, and 92.6 % for execution.

This suggests a stark difference in philosophy for reducing energy consumption in these two fields of applications with ICT favoring automatic adaptations over the behavioral change approach used in buildings.

### Answer to RQ1

The analysis of publication years shows changing trends in how software solutions for energy management address human behavior. While the mid-2010s saw an increase in related studies, recent years have experienced a decline, suggesting a possible plateau despite the ongoing significance of energy crises. The distribution of publication venues further reveals a lack of multidisciplinary platforms, with most studies appearing in conference proceedings rather than journals, limiting cross-disciplinary collaboration. Keyword analysis highlights a focus on energy reduction and behavioral monitoring, with less emphasis on behavioral change. This indicates a preference for software as a tool for data collection and processing rather than as a means for directly influencing behavior. Furthermore, a clear distinction exists between application areas: building-sector studies often employ user-centric feedback to modify behavior, while ICT solutions are more focused on automated optimizations with minimal user involvement. These findings point to potential opportunities for ICT interventions to adopt behavior-driven approaches seen in the building sector.

## 3.2. RQ2: what are the characteristics of these solutions to achieve energy optimization?

This section explores the key characteristics of different solutions used to achieve energy optimization. Depending on the specific context and optimization method, these approaches vary significantly to meet diverse needs. For instance, autonomous systems may rely heavily on complex sensor networks and algorithms, while user-dependent feedback systems require less back-end infrastructure but focus more on user-friendly interfaces. The goal here is to analyze the contrasting requirements of these approaches across various fields and synthesize the tools and techniques best suited for targeting energy consumption.

Since our focus is on human behavior, we will specifically examine papers that involve human intervention in energy optimization strategies. A significant portion of these studies employs 'feedback' approaches, where users are provided with insights about their energy consumption. These feedback mechanisms vary widely, from simple, general suggestion leaflets to highly personalized digital feedback that tailors recommendations based on individual user behavior. By focusing on these human-centric interventions, we aim to understand how feedback can influence user actions and contribute to energy efficiency.

Of the 43 papers that include, in some part, a human in the execution phase, 37 used a 'feedback approach', where they rely, at a minimum, on the human's will, influenced by the feedback provided, to execute a planned and automated intervention, and, at maximum, leave both the planification and execution entirely within the user's responsibility, with
<table><caption>**Table 10**
Publication count per feedback method and application field.</caption><thead><tr><th></th><th></th><th>TOTAL</th><th>Real-time</th><th>Historical</th><th>Disaggregation</th><th>Normative</th><th>Peer</th><th>Injunctive</th></tr></thead><tbody><tr><td rowspan="2">ICT</td><td>Amount</td><td>4</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Percentage</td><td></td><td>75.0 %</td><td>25.0 %</td><td>0.0 %</td><td>0.0 %</td><td>0.0 %</td><td>25.0 %</td></tr><tr><td rowspan="2">Building</td><td>Amount</td><td>33</td><td>27</td><td>20</td><td>19</td><td>5</td><td>10</td><td>2</td></tr><tr><td>Percentage</td><td></td><td>81.8 %</td><td>60.6 %</td><td>57.6 %</td><td>15.2 %</td><td>30.3 %</td><td>6.1 %</td></tr></tbody></table>

<table><caption>**Table 11**
Publication count per feature.</caption><thead><tr><th></th><th></th><th>TOTAL</th><th>Gamification</th><th>Social interaction</th><th>Goal-setting</th><th>Recommendation</th><th>Incentives</th><th>Forecasting</th><th>Actuators</th></tr></thead><tbody><tr><td rowspan="2">ICT</td><td>Amount</td><td>4</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Percentage</td><td></td><td>%</td><td>%</td><td>%</td><td>%</td><td>%</td><td>%</td><td>%</td></tr><tr><td rowspan="2">Building</td><td>Amount</td><td>33</td><td>9</td><td>12</td><td>4</td><td>16</td><td>7</td><td>5</td><td>6</td></tr><tr><td>Percentage</td><td></td><td>27.3 %</td><td>36.4 %</td><td>12.1 %</td><td>48.5 %</td><td>21.2 %</td><td>15.2 %</td><td>18.2 %</td></tr></tbody></table>

<table><caption>**Table 12**
Summary of reported energy reductions.</caption><thead><tr><th>Reference</th><th>Domain</th><th>Participants</th><th>Baseline</th><th>Control group</th><th>Normalized time (days)</th><th>Energy evolutions</th><th>Post-intervention</th></tr></thead><tbody><tr><td>[39]</td><td>Building</td><td>18</td><td>✓</td><td>X</td><td>30</td><td>-(6.6 % - 6.9 %)</td><td>X</td></tr><tr><td>[26]</td><td>Building</td><td>57</td><td>✓</td><td>✓</td><td>98</td><td>-13 %</td><td>0</td></tr><tr><td>[11]</td><td>Building</td><td>6</td><td>✓</td><td>X</td><td>10</td><td>-22.25 %</td><td>-0.20 %</td></tr><tr><td>[24]</td><td>Building</td><td>431 hh</td><td>✓</td><td>✓</td><td>90</td><td>-7.02 %</td><td>X</td></tr><tr><td>[54]</td><td>Building</td><td>90</td><td>✓</td><td>X</td><td>90</td><td>-18.95 %</td><td>X</td></tr><tr><td>[55]</td><td>Building</td><td>18</td><td>✓</td><td>X</td><td>30</td><td>-25 %</td><td>-30 %</td></tr><tr><td>[57]</td><td>Building</td><td>17 hh</td><td>✓</td><td>X</td><td>28</td><td>-24 %</td><td>~</td></tr><tr><td>[63]</td><td>Building</td><td>8</td><td>✓</td><td>X</td><td>40</td><td>-9 %</td><td>X</td></tr><tr><td>[65]</td><td>ICT Software</td><td>30</td><td>X</td><td>✓</td><td>40</td><td>-25.30 %</td><td>X</td></tr></tbody></table>

<table><caption>**Table 13**
Questionnaires, participants labeled with an asterisk when considered as households.</caption><thead><tr><th>ID</th><th>Amount</th><th>Participants</th><th>Data</th></tr></thead><tbody><tr><td>[41]</td><td>1</td><td>7</td><td>Qualitative: Behavior, Satisfaction and usage.</td></tr><tr><td>[48]</td><td>2</td><td>528; 100</td><td>Qualitative: Demographic and Energy.<br>Qualitative: Design</td></tr><tr><td>[64]</td><td>1</td><td>15</td><td>Qualitative: Design, energy, and Habits.</td></tr><tr><td>[45]</td><td>1</td><td>98</td><td>Quantitative and qualitative: Behavior,<br>Demographic, Energy, Habits, Satisfaction and<br>Usage.</td></tr><tr><td>[49]</td><td>1</td><td>29</td><td>Qualitative: Habits.</td></tr><tr><td>[53]</td><td>1</td><td>10</td><td>Qualitative: Satisfaction.</td></tr><tr><td>[26]</td><td>1</td><td>36</td><td>Qualitative: Behavior, Energy and Satisfaction.</td></tr><tr><td>[24]</td><td>2</td><td>431*; 380*</td><td>Qualitative: Demographic and Energy.<br>Qualitative: Energy.</td></tr><tr><td>[55]</td><td>1</td><td>18</td><td>Qualitative: Behavior, Design and Habits.</td></tr><tr><td>[57]</td><td>1</td><td>15*</td><td>Qualitative: Behavior, Demographic and Habits.</td></tr><tr><td>[61]</td><td>2</td><td>41; 24</td><td>Quantitative and qualitative: Demographic,<br>Energy and Habits. Qualitative: Behavior,<br>design, Energy, Habits and Usage.</td></tr><tr><td>[65]</td><td>1</td><td>30</td><td>Qualitative: Demographic, Energy and Habits.</td></tr></tbody></table>

<table><caption>**Table 14**
Interviews, participants labeled with an asterisk when considered as households.</caption><thead><tr><th>ID</th><th>Amount</th><th>Participants</th><th>Data</th></tr></thead><tbody><tr><td>[62]</td><td>1</td><td>17</td><td>Qualitative: Behavior, Energy, Design and Habits</td></tr><tr><td>[26]</td><td>1</td><td>36</td><td>Qualitative: Behavior, Design,<br>Energy, satisfaction and Usage</td></tr><tr><td>[11]</td><td>1</td><td>6</td><td>Qualitative: Behavior, Design,<br>Habits and Usage</td></tr><tr><td>[57]</td><td>1</td><td>4*</td><td>Qualitative: Behavior and habits</td></tr></tbody></table>

feedback as the only aid.

We have already seen in the previous section that there is a contrast in the role of human intervention within the building field of

<table><caption>**Table 15**
Focus groups.</caption><thead><tr><th>Study ID</th><th>Amount</th><th>Participants</th><th>Data</th></tr></thead><tbody><tr><td>[48]</td><td>1</td><td>6</td><td>Qualitative: Energy and habits</td></tr></tbody></table>

application, which is reflected in this section with 84.6 % of building papers using a 'feedback' approach, compared to 30.8 % of ICT papers. We decomposed the feedback approaches according to the taxonomy described in the methodology, with the number of publications per feedback narrative in Table 10 and per feedback features in Table 11. The data shows that both fields predominantly rely on *real-time* and *historical* feedback methods, though the extent of their use varies. In ICT, *real-time feedback* is present in 75 % of the publications, whereas in the building sector it appears in 81.8 % of the studies. This highlights the importance of real-time feedback across fields in influencing user behavior by providing immediate insights into energy use. Similarly, *historical feedback* is common in 60.6 % of building studies but is only employed in 25 % of ICT studies. Historical feedback allows users to track consumption trends over time, supporting more informed decisions about energy use in the long term. However the building sector demonstrates a wider variety of feedback methods compared to ICT. In the building sector, *disaggregation*, which breaks down energy use by appliances or areas, is used in 57.6 % of the publications, while it is entirely absent in ICT. Similarly, *normative feedback*, which compares a user's consumption to a reference group or standard, appears in 15.2 % of building studies but is not used in ICT. Additionally, *peer comparison* (30.3 %) and *injunctive feedback* (6.1 %) are more prevalent in the building sector. These methods leverage social and comparative feedback to drive energy-efficient behaviors. The absence of these methods in ICT suggests that energy optimization strategies in ICT may rely more on automated, system-level interventions rather than behavior-driven optimizations. In summary, the building sector emphasizes a more diverse and comprehensive range of feedback approaches aimed at
<caption>**Table 16**</caption>

Experiments, participants labeled with an asterisk are households and experiment days marked with two asterisks are a minimum.

<table><thead><tr><th>ID</th><th>Participants</th><th>Type</th><th>Control Group</th><th>Pre- intervention (days)</th><th>Intervention (days)</th><th>Post- intervention (days)</th></tr></thead><tbody><tr><td>[32]</td><td>NaN</td><td>Simulation</td><td>FALSE</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>[34]</td><td>NaN</td><td>Simulation</td><td>FALSE</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>[38]</td><td>NaN</td><td>Simulation</td><td>FALSE</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>[47]</td><td>NaN</td><td>Simulation</td><td>FALSE</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>[58]</td><td>NaN</td><td>Simulation</td><td>FALSE</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>[59]</td><td>NaN</td><td>Simulation</td><td>FALSE</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>[39]</td><td>18</td><td>Field experiment</td><td>FALSE</td><td>30</td><td>30</td><td>NaN</td></tr><tr><td>[43]</td><td>6</td><td>Field experiment</td><td>FALSE</td><td>NaN</td><td>14</td><td>NaN</td></tr><tr><td>[44]</td><td>UNSPEC</td><td>Field experiment</td><td>FALSE</td><td>NaN</td><td>UNSPEC</td><td>NaN</td></tr><tr><td>[45]</td><td>98</td><td>Field experiment</td><td>TRUE</td><td>NaN</td><td>1</td><td>NaN</td></tr><tr><td>[49]</td><td>20</td><td>Field experiment</td><td>FALSE</td><td>1</td><td>7</td><td>NaN</td></tr><tr><td>[51]</td><td>30</td><td>Field experiment</td><td>FALSE</td><td>UNSPEC</td><td>UNSPEC</td><td>UNSPEC</td></tr><tr><td>[53]</td><td>10</td><td>Field experiment</td><td>FALSE</td><td>NaN</td><td>70</td><td>NaN</td></tr><tr><td>[26]</td><td>57</td><td>Field experiment</td><td>TRUE</td><td>30</td><td>98</td><td>56</td></tr><tr><td>[11]</td><td>6</td><td>Field experiment</td><td>FALSE</td><td>UNSPEC</td><td>10</td><td>57</td></tr><tr><td>[24]</td><td>431*</td><td>Field experiment</td><td>TRUE</td><td>UNSPEC</td><td>90</td><td>NaN</td></tr><tr><td>[54]</td><td>90</td><td>Field experiment</td><td>FALSE</td><td>100</td><td>90</td><td>NaN</td></tr><tr><td>[55]</td><td>18</td><td>Field experiment</td><td>FALSE</td><td>30</td><td>30</td><td>81</td></tr><tr><td>[57]</td><td>17*</td><td>Field experiment</td><td>FALSE</td><td>14</td><td>28</td><td>30</td></tr><tr><td>[61]</td><td>24</td><td>Field experiment</td><td>FALSE</td><td>NaN</td><td>29</td><td>NaN</td></tr><tr><td>[63]</td><td>8</td><td>Field experiment</td><td>FALSE</td><td>40</td><td>40</td><td>NaN</td></tr><tr><td>[65]</td><td>30</td><td>Field experiment</td><td>TRUE</td><td>NaN</td><td>40**</td><td>NaN</td></tr><tr><td>[66]</td><td>UNSPEC</td><td>Field experiment</td><td>FALSE</td><td>60</td><td>210</td><td>NaN</td></tr><tr><td>[37]</td><td>3</td><td>Controlled</td><td>FALSE</td><td>NaN</td><td>1</td><td>NaN</td></tr><tr><td>[50]</td><td>10</td><td>Controlled</td><td>FALSE</td><td>NaN</td><td>1</td><td>NaN</td></tr></tbody></table>

influencing user behavior, while the ICT sector relies more narrowly on real-time feedback. This difference suggests a stronger focus on user engagement in the building sector, where behavior-driven optimization is more prominent, compared to the more technology-driven optimization seen in ICT.

This lack in diversity is also reflected in the implementation of persuasive features and metaphors. Gamification and social interaction, for instance, are completely absent in ICT studies, whereas they are present in 27.3 % and 36.4 % of building studies, respectively. By making energy optimization more engaging, these features likely encourage behavior change through competition, rewards, or community-based motivation. Their absence in ICT may suggest both (1) that incorporating these interaction-based features poses a challenge in this field and (2) that interventions are less user-focused, prioritizing other facets, such as automated system responses or back-end optimizations, over direct user engagement. Likewise, goal-setting (12.1 %) and incentives (21.2 %) are more common in the building sector, reflecting a greater reliance on motivational drivers to encourage energy efficiency. However, goal-setting is not used—not due to technical limitations but rather because of the short-term nature of ICT feedback, which contrasts with the longer cycles seen in building approaches. This shorter feedback cycle may reduce the relevance of long-term strategies like goal-setting, as ICT interventions tend to prioritize automated responses and back-end optimizations over sustained user engagement. The contrast in incentives is nuanced and should be interpreted cautiously. Since we consider immaterial rewards, like game currency, as incentives, the contrast in incentives closely aligns with the contrast in gamification. In the few cases where incentives are financial and offered outside of a gamified context, we argue they may be counter-productive due to the overjustification effect—a phenomenon in which introducing an external incentive, such as money, for an activity that a person already finds intrinsically rewarding can diminish their intrinsic motivation [27]. Additionally, the building sector demonstrates more frequent use of recommendations (48.5 %), forecasting (15.2 %), and actuators (18.2 %), likely because its diverse energy use cases benefit from tailored suggestions and forecasts that guide user actions effectively. ICT applications, by contrast, include recommendations and actuators only sparingly, reflecting a greater reliance on automation over user-driven interventions. This limited use may not only indicate a lack in widely accepted standards but also suggest a gap in end-user-oriented

guidelines for sustainable computing.

### Answer to RQ2

The characteristics of solutions for energy optimization vary depending on the sector and the role of human intervention. In human-centered strategies, feedback approaches are central, with building sector solutions incorporating diverse feedback methods (real-time, historical, normative, peer comparison) and persuasive features like gamification, social interaction, and goal-setting. These approaches are designed to engage users actively and encourage behavior-driven energy optimization. In contrast, ICT solutions focus more on real-time feedback and rely heavily on automated, system-level interventions with limited user interaction. Overall, the building sector uses a wider range of feedback techniques and motivational strategies, while the ICT sector emphasizes technological optimization over user engagement. We believe this highlights the opportunity to incorporate user-centric feedback mechanisms in ICT, which could drive behavioral change and reduce energy consumption by encouraging users to make more informed and conscious decisions about their software usage.

### 3.3. RQ3: to what extent are these approaches tested?

Studying the approach by which solutions are tested when humans are involved is crucial because human behavior and preferences play a significant role in the success of energy management optimization interventions. Field studies provide real-world insights into how users interact with the solutions in their natural environments, capturing authentic behaviors and challenges that controlled lab settings may overlook. Incorporating surveys and interviews allows researchers to
gather qualitative feedback, understand user experiences, motivations, and barriers in greater depth. This comprehensive approach ensures that solutions are not only technically effective but also user-centered, increasing their potential for adoption and long-term impact. The surveys conducted and the type of data produced were classified according to the definitions proposed by Colin Robinson and Kieran McCartan [29]. We have extracted the types of surveys conducted, the number of participants, the type and nature of data produced from each paper to facilitate both comparison between publications and to answer the research question. The result of this extraction and classification can be visualized in Tables 13, 14 and 15.

A similar treatment was applied to classify experiments and normalize experiment lengths to facilitate comparison between publications. We sorted experiments into three distinct classes: simulation, controlled and field experiment. Not all experiments were explicitly described so when exact data was not available and it was possible, we would make approximate as accurately as possible. Experiments that lasted less than a day were rounded up to a day, *months* were averaged to 30 days, a *semester* was considered to be 15 weeks (105 days), and *several days* was rounded up to 7 days. When the publication did not give enough information on how their experiment was conducted and we could not confidently estimate values then we left the cell unspecified (UNSPEC). The result of this extraction and classification can be visualized in Table 16.

Of the total 54 publications, only 7 (13.0 %) study behavioral change through the surveys conducted on participants (as seen on Table 13 and 14 where [11,26,41,45,55,57,61] describe surveys containing questions related to the behavioral change) and only 4 (7.4 %) also do so through measurements in their experiments. So, despite a majority of publications relying on human behavior, only a small minority actually studies the behavioral impact of their approach.

To understand to what extent these approaches are tested we can study the experiments conducted in Table 16. Of the total 54 publications, 25 (46.3 %) describe an experiment they have conducted of which 6 (24.0 %) are simulations, 2 (8.0 %) are controlled and the rest (68 %) are field experiments. While simulations have the benefit of being faster and more cost-efficient we believe experiments on humans are more adapted to studying the complex nature of human behavior and behavioral change. Of the 19 experiments conducted on humans, only 4 have control groups (21.1 %) which is necessary to limit the impact of unaccounted variables on the results of an experiment. These experiments have on average 51 participants but this average is skewed by an experiment with hundreds of participants as only 4 (23.5 %) of the 17 experiments that explicitly state the number of participants have more than 50 participants. If we consider the median of 20 participants we can see that a majority of the experiments have a relatively low participant count which impacts the validity of results produced. These experiments last on average 50 days with a median duration of 30 days which we find to be an appropriate duration. What these experiments are truly lacking, as seen previously, are post-intervention monitoring to study behavioral change and pre-intervention baseline measurements (Table 12). For the previous we found only 4 of 16 field experiments (25.0 %) included post-experience monitoring while for the latter we found only 10 of 16 (62.5 %) field experiments that included baseline measurements, of which 3 (30 %) did not clearly specify how long.

## Answer to RQ3

The extent to which energy optimization approaches involving human intervention are tested varies significantly. Of the 54 publications analyzed, only 7 (13.0 %) study behavioral change through surveys, and only 4 (7.4 %) measure behavioral change through experiments. Although a majority of the studies rely on human behavior, only a small proportion directly assess its impact. Regarding experiments, 46.3 % of publications conducted experiments, with a predominance of field experiments (68 %) over simulations (24 %) and controlled experiments (8 %). However, the majority of human experiments (78.9 %) lack control groups, and many have relatively small participant counts, which limits the robustness of their findings. Additionally, post-intervention monitoring and pre-intervention baseline measurements are often lacking, further limiting the depth of insights into the long-term behavioral impacts of energy optimization strategies.

Having answered our initial research questions, we have synthesized and grouped our findings in the following section.

## 4. Findings

From our study of existing literature, we draw the following observations:

### 4.1. RQ1: *how do software solutions for energy management account for human behavior?*

#### 4.1.1. F1 trends in human behavior research for energy management

The analysis of publication trends reveals a shift in the focus on human behavior in energy management. While the mid-2010s saw a surge in studies addressing human behavior in software solutions for energy optimization, recent years have seen a decline, indicating a potential plateau in research despite the growing significance of energy crises. Furthermore, the distribution of publication venues highlights a lack of prominent multidisciplinary platforms for this research, with the majority of studies appearing in conference proceedings rather than journals.

#### 4.1.2. F2 dominance of residential and commercial building sectors

The relation between human behavior and energy consumption is studied mostly in the context of occupant behavior in the residential/commercial sector as seen with the majority (75.9 %) of publications in this survey.

#### 4.1.3. F3 limited behavioral interventions in ICT

There is a significant contrast on achieving energy reduction through behavioral change between ICT and the building sector. While a majority (72.2 %) of publications studying occupant behavior in buildings reduced energy consumption through behavioral change, only a minority of publications (23.1 %) in ICT did so.

#### 4.1.4. F4 focus on hardware over software in ICT

When the relation between user behavior and energy consumption in ICT is studied, publications tend to focus on reducing hardware energy consumption rather than software energy consumption.
## 4.2. RQ2: what are the characteristics of these solutions to achieve energy optimization?

### 4.2.1. F5 role of feedback and persuasive techniques in achieving behavioral change

To achieve behavioral change a majority of solutions depends on the use of various forms of eco-feedback while a minority also depends on the use of persuasive methods that tend build upon feedback like gamification and social interactions.

### 4.2.2. F6 sector differences in feedback and motivation

The building sector employs a broader range of feedback techniques and motivational strategies to engage users, emphasizing behavior-driven energy optimization. In contrast, the ICT sector focuses more on technological optimization and automated solutions, with less emphasis on user engagement.

## 4.3. RQ3: how comprehensively are these solutions tested?

### 4.3.1. F7 limited measurement of behavioral change

Behavioral change is poorly measured in many studies, with only 13.0 % of publications assessing the effects of interventions through surveys, and an even smaller 11.1 % conducting post-intervention energy consumption monitoring.

### 4.3.2. F8 lack of human subject experiments

A majority of publications (64.8 %) did not involve experiments with human subjects, highlighting a reliance on theoretical or simulation-based studies rather than real-world, human-centered experimentation.

### 4.3.3. F9 insufficient scientific rigor in experiments

Many experiments lack the necessary scientific rigor, with only 21.1 % of studies employing a control group to minimize external influences. Additionally, 35.3 % of experimental protocols were poorly described, making it difficult to extract reliable parameters or replicate the experiments, undermining the robustness of the findings.

### 4.3.4. F10 small participant sample sizes

Experiments often feature small sample sizes, limiting the generalization of the findings.

We were able to use these findings to propose a set of recommendations for further works in the field with a focus on reducing energy consumption through human behavior and present these recommendations in the next section.

## 5. Threats to validity

In this study, we classify threats to validity based on the categorization framework outlined by Wohlin et al. [70], which is widely used in empirical software engineering research. This classification includes construct validity, internal validity, external validity, and conclusion validity. By following this framework, we aim to provide a structured and transparent discussion of potential biases and limitations in our study.

**Construct validity** may be influenced by the classification of papers, which was based on manual interpretation of abstracts and keywords. Although a standardized approach was followed, variations in how human behavior and energy efficiency are addressed across studies could lead to inconsistencies in categorization. To mitigate this, we implemented a consensus-driven process, where multiple researchers reviewed and discussed uncertain papers. This collaborative approach helped reduce the risk of misclassification and ensured that papers were only included or excluded after reaching agreement.

**Internal validity** could be affected by selection bias. The study relied on screening abstracts, which might have led to the exclusion of relevant studies if the key contributions were not fully detailed in the

abstract. Additionally, manual filtering of a large dataset inherently carries the risk of human error. However, to address this, we employed a snowballing technique to identify additional relevant studies through references in the included papers, helping to capture any overlooked work. Furthermore, our consensus-based review of unsure papers ensured that studies were discussed and scrutinized before final decisions were made, further reducing the likelihood of improper inclusion or exclusion. **External validity** may be limited by the choice of databases and the focus on English-language publications. This could have resulted in the omission of significant research published in other languages or in less prominent venues. Moreover, the study reflects the state of research at a particular point in time, and newer studies on software and human behavior in energy efficiency may not have been captured. **Conclusion validity** is potentially impacted by the relatively small number of studies included after filtering. While the dataset provided meaningful insights, the final selection may not fully represent the broad range of research in this domain. Additionally, manual data extraction introduces subjectivity, but the use of consensus-driven discussions helped mitigate the impact of individual biases on the synthesis of results. Efforts such as snowballing, cross-checking, and ensuring consensus on paper inclusion/exclusion decisions were implemented to minimize these threats. However, these limitations should still be considered when interpreting the findings of this study.

## 6. Recommendations

From the findings previously stated, we propose the following recommendations for future works:

### 6.1. R1 focus on software energy consumption and behavioral change

Our findings (F3 and F4) reveal a significant gap in optimizing energy reductions in the ICT sector, particularly at the software level. While the building sector has seen success in using behavioral change to reduce energy consumption (72.2 % of studies), only a minority of ICT studies (23.1 %) have applied behavioral interventions to achieve similar results. Furthermore, the focus within ICT research has largely been on hardware optimization, with little attention given to the energy impact of software usage. We recommend that future research address this gap by raising awareness among users about the environmental impact of their software usage and promoting greener practices. This could drive both users and developers to adopt more energy-efficient software solutions, aligning with the growing demand for sustainability.

### 6.2. R2 adapting building sector feedback systems to ICT

We found (F6) that building sector employs a broader range of feedback techniques for encouraging energy-saving behavior, while the ICT sector lacks emphasis on user-driven feedback mechanisms. We recommend adapting successful feedback systems from the building sector for use in ICT contexts. For example, energy consumption could be monitored on an individual-user basis in office environments, broken down by application, and delivered as feedback to users daily. This approach would provide users with more detailed insights into their behavior, enabling them to adjust their usage patterns for greater efficiency.

### 6.3. R3 better exploitation of qualitative data through interviews and focus groups

We found (F7) that many studies fail to explore the qualitative aspects of behavioral change, often relying solely on surveys. We recommend integrating interviews and focus groups into research methodologies, as they offer richer insights into the motivations and experiences of participants. In the context of eco-feedback systems, for example, interviewing participants could reveal which features were
most effective in driving behavior change. Focus groups could also be
instrumental in designing user-friendly interfaces that improve the user
experience and engagement.

6.4. R4 enhance experimental precision

The findings (F9) suggest that many experiments lack the necessary scientific rigor, with some lacking control groups (21.1 %) and having poorly described experimental protocols (35.3 %). We recommend implementing more precise experimental protocols, including randomizing groups and analyzing their homogeneity to ensure that observed differences are due to the intervention and not other variables. For example, in eco-feedback trials, random assignment to intervention and control groups would help eliminate biases and allow for more reliable conclusions about the effectiveness of the interventions.

6.5. R5 increase sample size and diversity

Our findings (F10) indicate that many studies suffer from small participant sample sizes, which limits generalization. We recommend increasing both the sample size and the diversity of participants in future studies, representing a broader demographic (age, gender, socioeconomic status, etc.). A larger and more diverse sample size not only enhances the statistical power of the study but also ensures the results are more representative of the general population, making the findings more applicable in real-world scenarios.

6.6. R6 monitor long-term behavioral change and rebound effects

We found (F7) a significant gap in the literature is the lack of long-
term monitoring of behavioral change post-intervention. We recom-
mend continuing to track users' energy consumption behavior after the
intervention phase to better understand the sustainability of the
changes. Additionally, it's crucial to examine both direct and indirect
rebound effects (where reductions in energy consumption lead to
increased consumption elsewhere) through surveys. These rebound ef-
fects can undermine energy-saving efforts and highlight the importance
of studying not only the immediate impact but also the long-term con-
sequences of behavior change in energy consumption.

7. Conclusion

In conclusion, we aimed at conducting a systematic mapping study on the state on software publications that considers human behavior while striving for energy efficiency. We have conducted our search across 5 different databases: *IEEE, ACM, Web of Science, Science Direct and Scopus*. We have followed the search process as defined by *Petersen et al.* [8,9]. Through manual filtration according to our defined inclusion and exclusion criteria and snowballing we were able to reduce the corpus from 4958 results to 54 publications.

We sought to study the trends and limits of the current state of the art
and we have found a lack of prominent journals and conferences with
papers part of the studied corpus majorly being dispersed among unique
sources. A majority of publications were applied to the build sector with
75.9 % of papers thriving for energy efficiency in the residential or
commercial sector. Of the remaining 24.1 % of publications applied to
ICT we found a significant 69,2 % to focus on optimizing hardware use
through automatic adaptations and throttling of components or reduced
use altogether which leaves software energy consumption as the most
scarcely studied field of application. We have also found a stark differ-
ence on how human behavior is accounted for with the building sector
tending to favor interventions on occupant behavior (around 78 %)
while ICT tend to favor autonomous interventions with user behavior as
a studied variable (around 75 %). Finally we've found that many ex-
periments were not conducted with the necessary level of precision
required for scientific investigation with only 46.3 % of papers

describing their experimental protocol of which 76 % were conducted
on human participants and a significant minority we considered to lack
crucial details on how their experiments were conducted. On average
experiments were short and conducted on a small population which,
with the addition of a lack of randomized control groups and baseline
measurements, contribute to risks of validity.

Based on our findings, we recommend that future research focus on reducing software energy consumption through behavioral change by adapting eco-feedback and persuasive technology approaches that have shown promise in smart-home environments. While the idea of promoting behavioral change is well-known, there is a significant opportunity to tailor these strategies specifically for the ICT sector. We also encourage more rigorous experimental designs, including randomization of experimental groups, the inclusion of control groups, comprehensive baseline measurements, and clearly defined methodologies. These practices are crucial for producing reliable results. Additionally, we suggest expanding the participant pool to ensure a more representative sample, which will enhance the validity and statistical power of the findings. Finally, given the critical role that feedback systems play in driving behavioral change, we propose conducting a comprehensive survey on feedback systems within the context of energy management. This could provide valuable insights and best practices that can further inform efforts to reduce software energy consumption in the ICT sector.

CRediT authorship contribution statement

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

Declaration of competing interest

The authors declare the following financial interests/personal re-
lationships which may be considered as potential competing interests:
Thomas Zaragoza reports financial support was provided by French
National Research Agency under Grant ANR-22-CE25-0010. If there are
other authors, they declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.erss.2025.103991.

Data availability

Data will be made available on request.

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