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Treating Common Problems Observed
During Smart Building Control Real-Life
Testing: Sharing Practical Experience

Georgios Karatzinis¹,², Iakovos Michailidis¹,², Asimina Dimara¹,³(✉), Aliki Stefanopoulou¹,², Vasileios Georgios Vasilopoulos¹,², Stelios Krinidis¹,⁴, Christos-Nikolaos Anagnostopoulos³, Elias Kosmatopoulos¹,², Dimosthenis Ioannidis¹, and Dimitrios Tzovaras¹

¹ Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece
{gkaratzi,michaild,adimara,alikstef,vvasilop,krinidis,kosmatop,djoannid, Dimitrios.Tzovaras}@iti.gr

² Department of Electrical and Computer Engineering, Automatic Control Systems and Robotics, Democritus University of Thrace (DUTH), Xanthi, Greece

³ Department of Cultural Technology and Communication, Intelligent Systems Lab, University of the Aegean (UoA), Mytilene, Greece
canag@aegean.gr

⁴ Management Science and Technology Department, International Hellenic University (IHU), Kavala, Greece

**Abstract.** Building energy management systems (BEMSs) have attracted much attention in recent years due to their potential to reduce overall energy consumption, reduce electricity bills, improve the efficiency of the electricity network and effectively manage renewable energy exploitation. However, testing the efficiency of such mechanisms in a real-life setup is not trivial, due to different unexpected incidents caused by the Building Automation and Control System (BACS) itself or by human participants who do not always conform to the experiment constraints, rendering comparability and conclusions extraction a quite tricky process. Maintaining tests' credibility is a multi-factor problem which the simulation tests are fail to emulate, limiting the transferability of simulation-based results into real-life practise. In this work, we present a conceptualized framework for real-life smart building control testing, that presents useful suggestions towards maintaining testing credibility, ensuring solid performance evaluation outcomes and reliability. Finally, the current work is a collection of practical lessons learnt during BEMS performance testing activities.

**Keywords:** Building Energy Management · Practical Experience · Building Automation and Controls · Real-Life Problems
# 1 Introduction

The building sector is inevitably a major energy consumer that causes almost 40% of the world's energy consumption, 40% of greenhouse gas emissions, and 70% of electricity usage [21]. The high amount of energy needed in buildings (residential and commercial) is directly connected with heating-cooling loads, domestic hot water, lighting, electrical appliances, and indirectly with insufficient insulation. The directives of the European Commission and legislative proposals set specific energy reduction goals in this direction. With the 2030 Climate Target Plan, the Commission proposes to increase the EU's ambition to reduce greenhouse gas emissions to at least 55% below 1990 levels by 2030 [5] which is a substantial increase compared with the previously defined target of at least 40% [1].

The aforementioned transition of energy systems has led to the intensified interest in smart grid technologies and implementations to minimize overall energy consumption, reduce electricity bills, improve the efficiency of the electricity network and effectively manage renewably generated energy [8]. Smart buildings can be realized as independent entities capable of managing their own operations to maintain residents' comfort while minimizing energy consumption. There is a trade-off between the concepts of energy consumption reduction and comfort improvement, as their objectives can be in conflict with each other. The main principal components that consist a smart building are: a) monitoring system to have a perception of its environment; b) communication system to connect devices; c) energy management system (EMS) to conduct energy-related decisions; d) intelligent management control system (IMCS) or, energy management control system (EMCS), with learning capabilities to formulate an optimization criterion and subsequently apply actions encapsulating total energy consumption, total energy generation, user thermal comfort, and maximum available flexibility; e) grid connection and communication to adjust load profiles of building consorting the balanced operation of the grid;

Numerous studies have been presented in the literature describing trends, risks and benefits of the Internet of Things (IoT) technologies in buildings [14], approaches that have been applied for building energy prediction [4], intelligent controllers towards sustainable buildings [19], the incorporation of machine learning and deep learning techniques [18] and artificial intelligence in energy management systems improvement [2].

However, real-world challenges arise when applying these control strategies in building energy management systems outside the deterministic simulation environment safety. Apart from tuning difficulties, there are barriers connected with handling noisy data, the needs for a large amount of data and a long time for training operations, high number of control parameters or high computational load, increased complexity in large-scale systems, and occasionally human expertise requirements. On top of these key barriers that have to be investigated and resolved, there are high operational costs on the electricity bill until acceptable control matching, while also there is an increased risk for potential catastrophic transients. Hence, there are multi-faceted and multi-level limitations that need
to be considered in real-world applications and subsequently during control eval-
uation tests. Within this context, key challenges for real-life smart building con-
trol evaluation tests presenting useful suggestions and usual practices towards
achieving sustainable building energy management are suggested. The value of
this study may also be extended by considering the emulation of similar inci-
dents within an appropriate simulation environment to test the BEMS evaluation
outcomes' robustness as well.

The rest of the manuscript is organized as follows: Sect. 2 presents real-life
challenges for monitoring, analysis and control solutions in BEMS, while also
potential credibility breaching points are defined. Section 3 extends further the
identification of challenges presenting incidents occurred in real-life experiments
and concludes suggesting mitigation actions towards real-life smart building con-
trol evaluation tests. Finally, Sect. 4 summarizes the main findings of this work
in the direction of sustainable building energy management.

## 2 Real-Life Challenges and Potential Credibility Breaching Points

Building control systems monitor and control the indoor climatic conditions
in buildings ensuring their operational performance as well as the safety and
comfort of occupants. Undoubtedly, a key challenge for the efficient integration
of a BEMS is the design of an effective and reliable control strategy. These
strategies could be either conventional or intelligent-based control systems. A
wide range of state-of-the-art approaches in intelligent-based control systems
for energy and comfort management in smart building has been presented in
[20] taking into consideration different aspects and parameters. The class of
intelligent control can be categorized in: a) Learning methods, b) Model-based
predictive control method, and c) Agent-based control systems.

**Conventional Controllers:** Standard control schemes that include thermostats, proportional-integral (PI) and proportional-integral-derivative (PID) approaches. Thermostats are mostly utilized for heating or cooling to a set point temperature but they may generate oscillations leading to wastage of energy in residential buildings [22]. PI and PID controllers are closed loop/feedback controls that are more efficient than thermostats [7]. However the lack of direct knowledge of the system and the proper gains' selection is a demanding task, otherwise the entire controlled system can be unstable. Thus, the controller parameters are tuned at a particular operating range with constant gains. Under noisy data and non-linear processes, large time delays occur.

**Learning Methods:** This class of controllers incorporates the concept of artificial intelligence alleviating the need for detailed models. Fuzzy logic controllers embody multi criteria strategies within expert knowledge where linguistic rules are generated using the system's observations and the designer's knowledge.
Fuzzy based approaches have been applied for heating purposes maximizing indoor thermal comfort capitalizing energy efficiency [9]. A more recent work presents a fuzzy based approach for cost, energy consumption and peak-to-average ratio reduction [11]. Artificial neural network (ANN) approaches are also used to bridge the gap between energy efficiency and indoor thermal comfort in buildings [3]. Reinforcement learning (RL) is also used for building energy management as the most prominent machine learning approach for control problems [16]. In RL methods the agent learn optimal actions using interaction with the system environment. Compared with MPC, RL is potentially model-free algorithm that takes advantage of real-time and historical data. Also, adaptive neuro-fuzzy inference systems are used combining the merits of fuzzy logic and neural networks for tasks such as building energy forecasting for cooling loads [6].

**Model-based Predictive Control Method:** This approach can cope with input/output signal constraints in multi-input and multi-output (MIMO) systems enabling the commands to use their full ability to set temperature limits for thermal comfort. Model predictive control (MPC) algorithms use nonlinear global optimization to manage the energy systems. In MPC, the system dynamics are reflected by a time-varying state space model derived from the energy system simulation model. MPC is based on a repeated real-time optimization of a mathematical system model impending disturbances (occupancy information, price tariffs, solar radiations) predicting the future system behavior that is considered within the optimization process. To carry out the control performance and optimization in BEMS, the MPC may be connected with a comprehensive model that is created in TRNSYS, EnergyPlus, Dymola or Matlab. An MPC approach for a combined thermal and electric energy system in a residential single-family building has been presented in [12]. This is the most popular approach for BEMS for shifting loads during peak hours with an ability to adapt to unexpected disturbances, including dynamic modelling and occupancy predictions. Also, it exploits better the thermal mass of building than conventional methods and shifting operations can be handled within a definite period. However, a challenging task is to select a suitable model while also some of the main limitations to be addressed are parameter tuning, choosing the cost function, and reformulating the optimal control problem.

**Agent-based Control Systems:** This is a burgeoning area of research that simulates the behavior and co-evolution of system components to explore complex adaptive systems (agents). The agents are distributed in the environment working on their assigned individual objectives with predefined behavior programs. However, they are capable of communicating with each other and with the environment. Agents have both perceiving and affecting qualities and they can be employed for modeling complex problems. Multi-agent systems (MAS) have been applied in energy management systems [13]. There is a need for open information exchange platforms between attached agents and the electrical appliances. Another issue is related with the huge amount of information associated with the process resulting in slow decision making. Lastly, an open challenge is the
assignment of agents to components as each agent needs to have a perception of which component is the one that they are responsible for. Table 1 presents a qualitative comparison of the open challenges for different involved control approaches in BEMS.

During the development of control strategies, the need for simulation tools is emerged to establish a clear, continuous and effective interaction with the buildings. Simulation platforms characterized as white box models offer a physical energy model of the building presenting its dynamic thermal behavior. These physical models are fully explainable and they use detailed building information considering different heat transfer models (conduction, radiation and convection) between building envelop and its surroundings. Well-known simulation packages are the EnergyPlus, TRNSYS (Transient System Simulation Tool), Dymola which supports the Modelica modeling language, DOE-2 and the less used Matlab/SIMULINK. Mathematical equations of heat and mass balance and all the energy transfer processes are used to create the system, leading to highly accurate models as most of the energy transfer processes are mapped into development of the building energy systems modeling structure [10]. Data-driven models or black box models are simpler but less accurate than the white box ones as they utilize less or no physical information of buildings. They use regression analysis

<table><caption>**Table 1.** Comparative analysis of the challenges emerge in control strategies</caption><thead><tr><td>Family of Controllers</td><td>Challenges</td><td>Indicative Works</td></tr></thead><tbody><tr><td>Conventional</td><td>Proper gain selection. Strictly bounded with system modelling. Inaccurate results due to sensitivity of controllers. Poor performance in noisy and non-linear processes.</td><td>[7,22]</td></tr><tr><td>Learning-based</td><td>Need for lots of data especially in Fuzzy, ANN and ANFIS approaches. In particular Fuzzy and hybrid-fuzzy methods have human expert knowledge relation for the principal elements of the system like membership functions definition. Training efforts are high in ANN based approaches. Scalability issues in RL methods when learning and acting in high-dimensional state (and action) spaces. Also, a proper system environment has to be constructed and reward engineering actions are required. In Multi-Agent RL, each agent may only have partial information of the environment making real-life application difficult.</td><td>[3, 6, 9, 11, 16, 23]</td></tr><tr><td>MPC</td><td>Complexity of modeling and algorithmic tuning. Training and data collection efforts are high. Parameter tuning, choosing the cost function, and reformulation of the optimal control problem are needed.</td><td>[12,15]</td></tr><tr><td>Agent-based</td><td>Needs open information exchange platforms between attached agents and the electrical appliances. Huge amount of information associated with the process resulting in slow decision making. An open challenge is the assignment of agents to components. Real scale implementation difficulties due to the involvement of humans and requirement for advanced information system.</td><td>[13]</td></tr></tbody></table>
based on historical data between output of net energy consumption and inputs such as solar, wind, occupancy behavior and equipment scheduling information. They are adaptable for buildings without the need for detailed physical parameters, but they can be time consuming to record the variables in long term simulations. Data-driven approaches include ANN, recurrent neural networks (RNN), regression methods, support vector machines (SVM) and extreme gradient boosting (XGBoost) among others. The last simulation class is referred to hybrid models that try to leverage the advantages and limitations of both physical (white box) and statistical (black box) models, thus named grey box models. They simplify the description of the building heat transfer process, regarding modelling and calibration, and simulate building energy demand. Resistance-capacitance (RC) thermal network represents this modelling approach. An analytical description of physical energy and data-driven models in building energy prediction has been presented in [4].

Clearly, monitoring and analysis operations are not separated from building energy control. Data acquisition from adequate number of devices is critical for the whole operation. It is important to ensure that IoT applications and networks are available at any time to establishing proper monitoring, control and fault-tolerance mechanisms that enhance robustness [14]. The interruption of constantly operating services may lead to catastrophic results. Data collection, storage and processing is a crucial requirement, especially when there are multiple heterogeneous connected sensors and devices. As a result of the Big Data, proper approaches are required to reduce traffic in information flow. Also, algorithms for cleaning raw and/or missing data are needed to improve data quality including feature engineering to extract informative knowledge. Failure management and prioritization of tasks is a backup plan for communication downtime assisting in the direction of security reasons. From the user's side, analysed and simplified data have to be visualized in forms of charts and animations through Graphic User Interface (GUI) to enable user-in-the-loop inserting a closed interaction and feedback regarding comfort acceptance. This serves, also, as a useful information for online calibration of the control algorithm. Occupancy patterns, personalized comfort behaviors and different occupancy profiles can be inserted in the computational optimization methods to enhance both energy efficiency and the occupants' comfort conditions term [17]. Optimal sensor localization secures misleading data collection and assists the diagnostic ability of the BEMS.

Summarizing, the challenges and open issues regarding monitoring, analysis and control are:

## Monitoring and Analysis Challenges

- Development of a real-time data collection framework which stores, process and updates data from all available sensors and devices to have a clear perception of the under examination BEMS. Data cleaning and feature engineering are mandatory processes to extract informative knowledge feeding with appropriate data the predictive models.
- The IoT framework should integrate all available devices, sensors and networks to ensure managing uninterrupted data exchange. The integration of heterogeneous systems, data analytic tools and user-friendly GUI in the IoT system is crucial.

- Development of machine learning-based forecasting approaches for energy load prediction, energy consumption estimation, indoor temperature, weather conditions, energy prices and occupant's consumption behavior for the monitoring stage.

## Control Test Challenges

- Define a control strategy that does not violate the global objectives and constraints of building energy management. There is a general balance or a trade-off between energy reduction and thermal comfort. The comfortable conditions should be maintained while moving towards energy efficiency ensuring. Also there are other constraints that should not be violated like the capacity of energy sources and energy storage systems, waiting time of appliances, constraints related with demand response, reactive power support limits while also maintaining operational temperatures within limits.

- The control schemes should be adaptable, effective, easily tunable and transferable to be applied from already evaluated simulated scenarios to real-life buildings integrated into smart energy systems. These characteristics are even more important when there is limited time in performing real-life experiments. The ability to learn from limited number of samples is crucial for convergence, stability and performance reasons. Also, the controller has to deal with delays in sensor measurements or actuators providing a robust method that manages uncertainty in the problem. Practically, the control method should be able to learn inherently the dynamics of the building regarding these induced delays and their relationship. Also, the control action should be applied with the lowest latency.

## 3 Real-Life Practise Experience: Lessons Learnt from a Spanish and a Greek Testbed

In this section we extend further the task of identifying challenges in BEMS, from a practical perspective, presenting malfunctions occurred during real-life experiments. The indicative malfunctioning incidents are from two different cases, i.e., an office (located in Greece) and a residential apartment case (located in Spain). Both are fully retrofitted with Adaptable/Dynamic Building Envelopes (ADBE) façade. The only heating source (no central heating system nor other supportive heating components) for the apartment was two controllable electric heaters
(1.5kW maximum power each) coupled with remotely controllable smart plugs. In the office case, a central heating system takes place already and one controllable electric heater is added with 2kW maximum power. Control actions follow a switch ON/OFF configuration.

In those cases, the goal was to minimize tuning efforts of real-time experiments presenting a framework that avoids potential catastrophic transients and high operational costs on the electricity bill. Usually, model assisted online approaches are used that last days or weeks leading to severe tuning efforts, energy waste or potential catastrophic transients. Thus, it was decided to use the simulation output tests from the, already validated, simulation model in an offline learning manner, rendering control set-points, energy consumption and indoor temperature together; to ultimately tune the controllers' gains. The control model considered mixed fuzzy approach to allow smooth switching across different operational areas. The tuning setup considered the intelligent management control system module to optimize the applied control policy in an energy-efficient and thermally comfortable manner at the same time. Following an episodic approach, i.e., repeating an indicative simulative week (over a 5-day horizon) considering Spanish and Greek autumn weather conditions at every tuning iteration, the tuning process required less than 100 iterations. The reason for doing so was to eventually consider exogenous noise in the tuning process, according to the fluctuation of the weather conditions during these five simulation days. Details about the simulation tests and controller pre-training and specifications are not provided here. In this work, the objective is to present the conceptualization adopted towards a framework for real-life smart building control evaluation tests and the usual practices that arise upon these tests.

For this reason, we present indicative malfunctions that occurred. The first two incidents have been presented in the office case and the third one has been observed in the residential apartment case. Note that the desired temperature in all cases has been derived on the basis of occupancy profiles. In the office case, the desired temperature is extremely low other than working hours as any heating system was forbidden to operate.

- **Incident 1:** The office includes a central heating system that is already in place, as mentioned above. The occupants increased its set-point higher than the desired temperature that the control method of the remotely electric heater operates. Thus, as depicted in Fig. 1 the remotely controlled device has been activated only for a short period of time during early morning.

- **Incident 2:** This is a scenario where control signal is sent but it was never applied on the remotely controlled device located in office (see Fig. 2). This scenario performed during weekend where central heating system was shut down. Unfortunately, an occupant turned off the smart device that was in standby mode and the control signals never applied to change the status of the device.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Incident 1 - Office case</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. Incident 2 - Office case</figcaption></figure>

- **Incident 3:** In this case, there are two controllable devices, one located in the living room and the other located in an adjacent smaller room. Slightly after 14:01, a communication error occurs (Fig. 3), the control method is unable to communicate with the devices, the status of devices remains the same as prior to communication fault and subsequently the room temperature exceeds the desired temperature level leading to energy waste and potential catastrophic transients if left for unacceptable time frame.
<figure><img src="image_4.png" /><figcaption>Fig. 3. Incident 1 - Residential apartment case</figcaption></figure>

Based on the above depicted examples of incidents and aligned with the
already defined challenges in building energy control applications, the following
usual practices are added:

Common Mitigation Practices

- Integration of heterogeneous components is needed in cases where conventional appliances co-exist with smart ones. Especially when the conventional heating systems are not automated.

- Use non-intrusive sensors in optimal locations. Conceivable division of spaces into sub-areas to place sensors in the centre of these sub-areas. Otherwise, sensing devices close to windows or doors or to distant places will lead to misleading measurements. Smart meters, smart plugs and devices if possible to be hidden or not reachable. This is mainly for hotels, hospitals, offices, municipality buildings, public buildings and utilities.

- Mitigation actions should be foreseen when communication system and thus monitoring and control is down. Automated notifications are needed to alarm both users and building managers. At the same time a safe mode of control actions based on the last weather predictions and indoor measurements in relation with similar days in terms of occupancy profile is needed to operate in a conservative function. This is mandatory when control actions have been sent to smart plugs prior to communication interruption as the state of the devices will remain unchanged during the downtime. In the meantime the user should be able to verify locally via the GUI if the safe mode operates based on comfort desirability, otherwise a universal local signal of deactivation of all remotely controllable devices is essential to avoid catastrophic conditions accepting thermal comfort penalty.
# 4 Conclusions

To sum up, the building sector is responsible for a significant portion of the world's energy consumption and greenhouse-gas emissions, which has led to the need for energy reduction goals and the development of smart building technologies. Smart buildings are capable of managing their operations to maintain comfort while minimizing energy consumption, but there is a trade-off between energy consumption reduction and comfort improvement. In this paper, through an analysis of these challenges, useful suggestions and usual practices are presented to achieve sustainable building energy management. However, it is important to acknowledge that further investigation and resolution of key barriers are necessary to fully realize the potential of smart building technology. By considering multi-faceted and multi-level limitations during control evaluation tests, we can continue to improve the efficiency and sustainability of building energy management systems, ultimately contributing to a more sustainable future.

**Acknowledgements.** This work is partially supported by the PRECEPT project funded by the European Union's Horizon 2020 under Grant Agreement No. 958284 https://www.precept-project.eu/ and the Plug-N-Harvest https://www.plug-n-harvest.eu/ project funded by the European Union's Horizon 2020 under Grant Agreement 768735.

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