1. Introduction
Efficient management of energy resources in the built environment is crucial for addressing contemporary challenges related to energy sustainability and environmental impact. The building sector, accounting for a substantial portion of global energy consumption [
1], presents a significant opportunity for implementing strategies to enhance energy efficiency and reduce carbon emissions. For instance, in Morocco, it reached as high as 33% [
2]. Improving a building’s energy efficiency involves a number of approaches, such as insulation, upgraded window glazing, and economizer controls, which can significantly reduce energy consumption [
3,
4,
5,
6,
7,
8,
9]. Geographical location significantly influences the energy demands of HVAC systems, underlining the importance of global considerations in building design [
9], and HVAC systems alone may account for more than 60% of a building’s energy use [
10]. Control interventions demonstrate the potential for up to 34% energy savings, emphasizing the importance of optimized operation strategies [
11].
Renewable energy integration should be prioritized alongside efficiency improvements, with a detailed understanding of a building’s energy requirements being crucial for optimal resource management. Simplified building energy models are highly desirable for evaluating and diagnosing building systems effectively [
12]. Studies emphasize the need for holistic considerations in building design and energy management to achieve efficiency and cost-effectiveness [
8].
In the quest for energy sustainability, the effectiveness of building energy models emerges as a fundamental element, providing information on energy use patterns and facilitating informed decision-making. However, while white-box models offer valuable information, they often fail to capture the subtleties of actual building dynamics and occupant behavior, leaving a notable gap in decision-making when controlling and optimizing HVAC systems. Traditional approaches like PID controllers face inefficiencies and overshoot issues, particularly in buildings with slow thermal dynamics. This highlights the necessity for adaptive, data-driven strategies that leverage advancements in ML and MPC to optimize energy usage while ensuring occupant comfort. Furthermore, the integration of material solutions into energy management practices remains a relatively unexplored avenue in the current literature. As a result, the need for sophisticated modeling techniques, combined with advanced control methods such as MPC, which reconciles technical precision with real-world complexities, is becoming increasingly apparent.
In summary, this introduction positions our study within the broader discourse of energy sustainability, addressing critical gaps in building energy modeling, control methodologies, and hardware implementations. By delving into these aspects, our research aims not only to reduce the demand for heating and cooling, which collectively constitute the majority of energy consumption in the building sector, but also to monitor and manage the thermal comfort of occupants. This focus on ensuring occupant comfort, coupled with the robustness of our proposed solution, distinguishes our work from a previous study [
13] that concentrated on the feasibility of the proposed solution to reduce the energy furnished by HVAC systems. Finally, we want to catalyze the practical advances that will foster a future in which buildings guarantee both energy efficiency and the well-being of their occupants.
The structure of the remainder of this paper is arranged as follows. In
Section 2, we delve into the current state of the art regarding modeling techniques for buildings and control strategies for optimizing thermal comfort within building environments. In
Section 3, we present a concise introduction to the methodologies used to create the energy-building model. This section covers the creation of the NNMPC technique, the implementation process of the control law on the PYNQ board, and the data transfer between EnergyPlus and the PYNQ platform, where the NNMPC is applied.
Section 4 presents the outcomes of our research.
Section 5 presents a comparative analysis with findings from other studies focusing on energy reduction in buildings. To wrap up our discussion, in
Section 6, we offer our concluding remarks and findings.
4. Results
To validate the effectiveness of this control strategy, a comparative analysis was conducted between the NNMPC and the On/Off control law. According to the On/Off control law, the HVAC system operates at full capacity. For heating, it maintains a supply mass flow rate of 0.1 kg/s and a supply temperature of 50 °C. For cooling, it sustains a supply mass flow rate of 0.3 kg/s and a supply temperature of 13 °C. This process continues until the temperature surpasses the heating setpoint or falls below the cooling setpoint. The control law employs a 1 min sampling time, which aligns with the lowest time step available in EnergyPlus simulations. The building was simulated using EnergyPlus and was located in Casablanca, Morocco. The study considered two different years, 2006 and 2017, with outside temperature data shown in
Figure 7, and the room temperature data applying the On/Off control strategy in
Figure 8a,b.
On the other hand, the thermal mass of construction materials causes disturbances that have slow dynamics and a high moment of inertia. Consequently, the NNMPC controller runs with a 10 min sample period. Between 0 and 0.1 kg/s is the range of the supply mass flow rate, and 50 °C is the temperature at which the supply air is kept for heating and cooling. Furthermore, the supply mass flow rate changes from 0 to 0.3 kg/s for cooling, with the supply air temperature set to 13 °C.
Temperature predictions from ANN are closely aligned with EnergyPlus calculations. Thanks to the NNMPC control strategy, EnergyPlus temperature measurements are managed consistently to stay, most of the time, around the target temperature (a range of approximately ±0.5 °C); see
Figure 9 and
Figure 10. Moroccan thermal regulations provide specific guidelines regarding temperature settings for heating and air conditioning systems. According to these regulations, the designated temperature setpoint for heating is 20 °C, while for air conditioning, it is set at 26 °C [
52]. Consequently, the range between 20 °C and 26 °C delineates our defined thermal comfort zone. In addition to the prescribed comfort range, we have incorporated an acceptable discomfort zone of 0.5 °C above the air conditioning setpoint and below the heating setpoint. This intentional extension allows for a degree of flexibility, particularly within our predictive control methodology.
Figure 11 and
Table 1 represent the energy consumed by heating and cooling for each month of the years 2006 and 2017, applying the On/Off control method and the NNMPC method, respectively, with the NNMPC calculated in the Desktop and PYNQ. For both years, September and January represent the most energy-intensive months for HVAC systems, in both control modes. In September 2017, we had a consumption of 139.65 kWh versus 173.93 kWh for NNMPC control and On/Off mode, respectively, which represents a reduction of 19.71%. For January, we had a consumption of 188.74 kWh versus 238.40 kWh for NNMPC control and On/Off mode, respectively, which represents a reduction of 20.83%. This energy reduction is close to that provided by the computer, where it represents 19.59% for September and 20.85% for January. During the year 2006, in January 2006, consumption was 78.64 kWh compared with 119.88 kWh for NNMPC control and On/Off mode, respectively, representing a reduction of 34.4%. For September 2006, consumption of 80.65 kWh versus 113.62 kWh for NNMPC control and On/Off mode, respectively, represents a reduction of 29.02%. This energy reduction is the same as the one provided by the computer.
For an annual analysis, in the year 2006, we had a heating consumption of 240.04 kWh versus 405.31 kWh for NNMPC control and On/Off mode, respectively, representing an annual reduction in heating energy consumption of 40.78%. For cooling consumption, it was 221.86 kWh compared with 356.54 kWh for NNMPC control and On/Off mode, respectively, representing a reduction of 37.77%. For the year 2017, we had a cooling consumption of 369.78 kWh versus 519.43 kWh for NNMPC control and On/Off mode, respectively, representing an annual reduction in cooling energy consumption of 28.81%. For heating consumption, it was 464.89 kWh versus 624.86 kWh for NNMPC control and On/Off mode, respectively, representing a reduction of 25.60%; see
Figure 12 and
Table 1.
Figure 13 illustrates the room temperature fluctuating within a range, primarily between 20 °C and 26 °C. However, it is noteworthy that the NNMPC, deployed on PYNQ, tends to shift the room temperature into the discomfort zone more frequently compared to the On/Off method. Specifically, in 2017, we observe that, under NNMPC control, 10.72% of the time, the temperature falls below 20 °C, with a deviation of 0.85% on average, which represents 0.17 °C, and with a maximum deviation of 7.80%, which represents 1.55 °C. For 14.77% of the time, it exceeds 26 °C, with a deviation of 0.91% on average, which represents 0.24 °C, and with a maximum deviation of 5.85%, which represents 1.16 °C.
Regarding the Desktop implementation, the values are notably closer, with 10.58% below 20 °C, with a deviation of 0.80% on average, which represents 0.16 °C, and with a maximum deviation of 4.17%, which represents 0.83 °C. Additionally, we have 14.86% above 26 °C, with a deviation of 0.89% on average, which represents 0.23 °C, and with a maximum deviation of 5.67%, which represents 1.13 °C. In contrast, with the On/Off method, the discomfort zone occurrences are lower, with only 2.53% below 20 °C, with a deviation of 0.68% on average, which represents 0.13 °C, and with a maximum deviation of 3.73%, which represents 0.74 °C. For 1.95% of the time, it exceeds 26 °C, with a deviation of 0.41% on average, which represents 0.1 °C, and with a maximum deviation of 2.82%, which represents 0.56 °C. Moreover, the NNMPC breaches the critical threshold of 19.5 °C only 0.2% of the time and goes above 26.5 °C merely 0.92% of the time, which remains within acceptable limits. Considering the predictive nature of the NNMPC, such deviations can be accommodated without significant inconvenience.
In the 2006 data, we observe that, under NNMPC control, 14.38% of the time, the temperature falls below 20 °C, with a deviation of 0.86% on average, which represents 0.17 °C, and with a maximum deviation of 6.79%, which represents 1.35 °C. For 12.04% of the time, it exceeds 26 °C, with a deviation of 0.97% on average, which represents 0.25 °C, and with a maximum deviation of 6.16%, which represents 1.23 °C. Regarding the Desktop implementation, the values are notably closer, with 14.41% below 20 °C, with a deviation of 0.82% on average, which represents 0.16 °C, and with a maximum deviation of 4.03%, which represents 0.8 °C. Additionally, we have 12.04% above 26 °C, with a deviation of 0.97% on average, which represents 0.25 °C, and with a maximum deviation of 6.16%, which represents 1.23 °C. In contrast, with the On/Off method, the discomfort zone occurrences are lower, with only 1.66% below 20 °C, with a deviation of 0.38% on average, which represents 0.07 °C, and with a maximum deviation of 2.56%, which represents 0.51 °C. For 1.52% of the time, it exceeds 26 °C, with a deviation of 0.4% on average, which represents 0.1 °C, and with a maximum deviation of 2.53%, which represents 0.5 °C. Moreover, the NNMPC breaches the critical threshold of 19.5 °C only 0.32% of the time and goes above 26.5 °C merely 1.02% of the time, which remains within acceptable limits.
Figure 14 depicts the deviation distribution of both the On/Off control law and NNMPC for the years 2006 and 2007. Additionally, it illustrates the deviation distribution for the computer implementation of the NNMPC solution and PYNQ. The deviation calculation is based on Equation (
11). In this context, the negative portion of the deviation distribution signifies instances falling outside the defined comfort zone. Specifically, during the heating season, negative values indicate deviations below the setpoint of 20 °C, indicating situations where room temperature falls below the desired level. Conversely, in the cooling season, negative values signify deviations exceeding the setpoint of 26 °C, reflecting instances where the temperature surpasses the desired threshold. Thus, negative values in the deviation distribution directly correspond to discomfort, manifesting as sensations of cold during winter and heat during summer. The deviation can be computed using the following equation:
Here,
i represents the index of the deviation. This equation defines the computation of deviation based on the specified conditions for the temperature setpoints and control status. We observe from
Figure 14 that the NNMPC method confines room temperature to within ±0.5 °C of the setpoint for both heating and cooling in 93% to 98% of cases. In contrast, the On/Off method surpasses the setpoint by 1.5 °C to 2.5 °C for heating and drops 2.5 °C to 3.5 °C below the setpoint for cooling. These deviations represent significant fluctuations in room temperature, potentially causing discomfort for occupants despite remaining within the established comfort zone. Conversely, the NNMPC method maintains a certain stability of room temperature around the setpoint, whether for heating or cooling.
The term PMV stands for Predicted Mean Vote. It is a metric used in thermal comfort analysis to predict how a person will perceive the thermal environment. PMV is based on a model developed by Fanger in the 1970s [
53,
54] and is widely used in building design, HVAC systems, and indoor environmental quality assessment.
The PMV index quantifies the predicted average thermal sensation of a large group of people exposed to a specific indoor environment. It takes into account various factors such as air temperature, radiant temperature, air speed, humidity, and clothing insulation. It operates on a 7-level thermal sensation scale, ranging from
to +3, as depicted in
Figure 15.
A PMV of 0 indicates a neutral thermal sensation, while values closer to +1 represent slightly warm conditions, and values closer to
suggest slightly cool conditions. A PMV of +2 signifies warm conditions, and
indicates cool conditions. Extremes of thermal sensation, such as PMV values of +3 or
, correspond to perceived hot or cold environments, respectively. Understanding and maintaining PMV within acceptable ranges are crucial for ensuring occupant comfort and productivity in indoor spaces. In indoor environmental quality assessment, three main categories are commonly used to gauge occupant comfort. In Category I, environments are considered highly satisfactory when the PDD is 10% or lower and when the PMV falls within the range of
to +0.5. In Category II, satisfaction levels are moderately satisfactory, with PPD ranging between 10% and 25%. PMV is deemed acceptable when it falls within the range of
to
or from +0.5 to +1. In Category III, environments are deemed unacceptable if the PPD exceeds 25%. PMV values beyond
or above +1 are considered unsatisfactory in this category [
55].
Clothing insulation and metabolic rate have a strong influence on thermal comfort [
56]. Clothing insulation specifies the level of insulation worn by occupants in a building. The thermal resistance of clothing materials directly influences thermal comfort and energy consumption within the building. During colder months, such as December to April, higher insulation values, ranging from 1 to 1.2 clo, are prescribed. Conversely, during warmer months, such as June to October, lower insulation values, ranging from 0.4 to 0.5 clo, are recommended, reflecting the lighter garments worn during warmer temperatures. For the months of May and November, average insulation values ranging from 0.7 to 0.8 clo are used, with
. By adjusting clothing insulation levels based on seasonal variations, occupants experience comfort throughout the year, contributing to a conducive indoor environment and overall building efficiency.
The internal gain of occupants within the space fluctuated throughout the day, totaling 131.8 W. This value was subject to modification by a fractional factor across distinct time intervals. The distribution of these factors delineates the fluctuating impact of internal gains throughout the day. Starting from the onset of the day until 06:00, the internal gain factor stood at 0.67. Between 06:00 and 07:00, the factor decreased to 0.45, indicating a reduction in internal gains. Continuing from 07:00 to 08:00, the factor further declined to 0.27. From 08:00 until 19:00, the factor stabilized at 0.3, representing a relatively steady level of internal gains during daytime hours. During the evening hours, from 19:00 to 21:00, the factor decreased to 0.21, reflecting diminished internal gains. Between 21:00 and 22:00, the factor experienced a slight increase to 0.33, suggesting a minor resurgence in internal gains. Finally, from 22:00 until midnight, the factor reverted to 0.67.
Natural ventilation, quantified as 1 ACH or 1 volume per hour (V/h), is a fundamental aspect of building design, contributing to indoor air quality and thermal comfort. This ventilation rate indicates how often the air in a specific zone is replaced by fresh outside air. A ventilation rate of 1 ACH or 1 V/h means that the entire volume of the space is renewed by outside air every hour. These ventilation strategies are essential for maintaining optimal indoor environmental conditions by expelling pollutants, regulating humidity levels, and promoting thermal comfort. In addition, natural ventilation promotes energy efficiency by reducing reliance on mechanical systems, in line with sustainable building practices. Implementing natural ventilation at a rate of 1 ACH or 1 V/h underscores a balanced approach to enhancing occupant well-being while promoting environmental stewardship in building design and operation.
Figure 16 and
Figure 17 depict the value and distribution of the PMV, respectively, for both the On/Off control law and NNMPC for the years 2006 and 2007.
Table 2 illustrates the distribution of PMV for the years 2006 and 2017 categorized by control strategies and implementation platforms. The table structure consists of four rows representing different PMV thresholds (
< PMV <
,
< PMV <
,
< PMV < 0.5, 0.5 < PMV < 1.0, and 1.0 < PMV < 3.0) and three columns displaying the distribution percentages under the NNMPC and On/Off control strategies implemented on both the PYNQ and Desktop platforms.
For the years 2006 and 2017, the table presents the percentage distribution of instances where the PMV falls within specified intervals for different control strategies and platforms. For the year 2006, no instances are recorded where PMV falls below
across all control strategies and platforms. However, for the year 2017, only 0.20%, 0.20%, and 0.22% of PMV values for NNMPC on PYNQ, NNMPC on Desktop, and On/Off control on Desktop, respectively, fall below
. Conversely, for PMV values between
and 0.5, the percentages are notably high, standing at 72.35%, 72.45%, and 83.75% for NNMPC on PYNQ, NNMPC on Desktop, and On/Off control on Desktop, respectively, in 2006. In 2017, the percentages remain significant, with values of 64.98%, 65.06%, and 76.77% for the same respective categories. Additionally, for PMV values between 0.5 and 1.0, the percentages in 2006 are 27.14%, 27.04%, and 16.07%, respectively, while in 2017, they amount to 32.80%, 32.75%, and 22.50%. PMV values exceeding 1.0 also show substantial differences, with percentages ranging from 0.17% to 2.01% across different strategies and platforms in both 2006 and 2017. Regarding the maximum and minimum PMV values, the data highlight a variation between the two years, with maximum PMV values ranging from 1.11 to 1.34 and minimum PMV values ranging from
to
across the platforms and strategies considered; see
Table 3.
The analysis reveals that, on the whole, the two control techniques converge on a value for PMV around the threshold of Category I, where environments are considered very satisfactory. However, upon PMV surpassing 1, the On/Off method marginally outperforms the NNMPC solution in meeting the thermal comfort criterion of PMV. It is worth noting that the optimal PMV values, which closely approach 0, tend to occur around a temperature of 22.5 °C. Remarkably, this temperature sits above the RTCM setpoint for heating and below it for cooling. Furthermore, the On/Off method adeptly steers the temperature closer to this optimal point (22.5 °C), thereby elucidating its superior performance in ensuring thermal comfort. However, the classic control method requires more energy to achieve its objectives, as evidenced by the Kernel Density Estimate (KDE) plots in
Figure 18. These plots offer insights into the distributions and relationships between the MSE and sensible energy needs for the NNMPC and On/Off control strategies across both years (2006 and 2017). Here, we aimed to assess the influence of the sensible thermal energy delivered by our HVAC system on the room temperature. In essence, we sought to quantify the change in room temperature following the supply of energy by the HVAC system. The MSE calculation method used here computes the average of the squares of the differences between the room temperature before and after the HVAC system’s energy supply. This approach enables us to understand the temperature variation induced by the HVAC system when it provides energy in the form of a mass flow rate of hot or cold air as required. The findings affirm that the NNMPC control strategy not only markedly diminishes our energy needs for heating and cooling to stay within the comfort zone, as demonstrated by the study’s outcomes, but also ensures that room temperature remains closely aligned with the setpoint, thereby guaranteeing consistent thermal comfort throughout the control process.
5. Discussion
Several initiatives have successfully reduced HVAC system energy consumption while ensuring occupant thermal comfort; see
Table 4. These studies typically employ their proposed solutions and compare them with basic On/Off control techniques. Replicating exact circumstances for comparison analysis is a real problem for experimental studies. The limitation arises from difficulties in reproducing precise environmental factors that may influence the thermal performance of a building. In contrast, simulation could provide a valuable avenue for overcoming this challenge by allowing researchers to recreate and control various conditions.
In the work conducted by Ref. [
16], based on heat transfer concepts similar to electrical systems, researchers created reduced-order systems using a frequency response technique. The findings showed that using a PI controller to regulate temperature effectively might save around 8% of energy when compared to standard On/Off control, all while preserving interior comfort. EPMPC was developed in Ref. [
57] and achieved substantial reductions in energy consumption during a one-week simulation, with heating energy reduced by 28.9% and cooling energy by 2.7%. Unlike traditional rule-based control systems, these enhancements were achieved without sacrificing occupant thermal comfort. The subsequent study by Ref. [
58] aimed to improve the energetic performance of a building situated in Benevento, Italy. To regulate the NZEB’s heating system, they used a framework based on simulation and optimization that integrated ANNs and ML models inside MPC. This framework used weather forecasts to determine the ideal setpoint temperatures, hence minimizing heating energy expenses and discomfort. Taking comfort penalties into account, they produced the Pareto front and chose the best option. In comparison to a fixed setpoint control, experiments carried out over a typical heating day showed a considerable daily energy consumption reduction of 26%.
Building automation was the main objective of the study by Ref. [
33], which evaluated the efficacy of an MPC system featuring adaptive ML-based control. The office and the theater were the two zones where this strategy was used, and the results were huge energy savings. In comparison to the initial control approach, they accomplished a 58.5% decrease in cooling energy usage within the office and a 36.7% drop in amphitheater air-conditioning power usage.
Implementing radial basis function neural networks, as proposed by Ref. [
59], is an effective strategy for achieving both thermal comfort and energy savings in public buildings. The approach optimizes a cost function intended to reduce energy usage while preserving the required comfort levels, and it uses the PMV index to assess thermal comfort. This approach comprises three crucial components: the implementation of radial basis function neural networks for prediction, an energy consumption-minimizing cost function, and the utilization of a discrete branch and bound optimization method. The application of this predictive modeling method is anticipated to yield substantial energy savings, surpassing 50%, with a range spanning from 41% to 77%.
In the study conducted by Ref. [
60], a comprehensive framework was developed to enhance occupants’ thermal comfort within HVAC operations. This framework is built upon several key components, including to gradually acquire customized thermal comfort profiles. A room-based distributed control strategy is incorporated into the process, distinct from traditional thermal zone-based approaches, and integrates seamlessly with legacy HVAC systems. This innovative approach reduced average airflow rates by a significant 39% on a daily basis, showcasing its efficacy in providing and maintaining thermal preferences based on the individual thermal comfort data of occupants.
The study conducted by Ref. [
61] aimed to prioritize interior thermal comfort while developing energy-efficient control strategies for HVAC systems in non-domestic buildings. SMC, basic On/Off control, and MPC based on the EnergyPlus model were the control strategies employed and evaluated in this study. The EnergyPlus model-based MPC, utilizing the state-space model, outperformed other strategies in terms of energy efficiency. During summers, it demonstrated an 11.94% greater energy savings compared to SMC and, in comparison to the On/Off command, it consumed 14.67% less energy. Additionally, for a setpoint of 23 °C during winters, compared to the On/Off command, the MPC consumed 17.20% less energy, and it used 19.89% less energy than SMC. The PMV-based setpoints, computed using an ANN, proved effective in reducing power consumption by 3.48% when integrated into an On/Off controller. The research findings highlight the potential of an EnergyPlus model-based MPC as an energy-efficient control technique for HVAC systems in non-domestic buildings. The strategy not only outperformed SMC and the basic On/Off strategy during summers but also demonstrated substantial energy savings during winters, without compromising occupants’ thermal comfort.
The primary objective of the research conducted by Ref. [
62] was to forecast occupant activity patterns and local weather in order to minimize energy usage and maintain room temperature setpoints. The study employed NMPC as a key strategy, showcasing significant measured energy reductions. During the heating season, the implemented NMPC resulted in a substantial 30.1% measured energy reduction compared to conventional scheduled temperature setpoints. Similarly, in the cooling season, there was a notable 17.8% reduction in energy consumption. These outcomes were obtained through experiments that lasted one week during the cooling season and two continuous months during the heating season. By leveraging occupant behavior predictions and local meteorological data, this approach effectively reduces energy consumption.
The goal of the study conducted by Ref. [
63] was to lower buildings’ cooling energy usage. By utilizing a constant temperature setpoint based on the occupancy method, the suggested technique results in a noteworthy 59% reduction in energy consumption. This indicates the effectiveness of integrating occupancy information into the control strategy to achieve substantial energy savings in cooling systems for buildings.
The adaptive controller, in the study conducted by Ref. [
64], demonstrated notable achievements in energy efficiency, with a 12.5% decrease in end-use energy for heating and a 15.3% reduction in summer. The study envisaged decentralized systems that inhabitants would control for climatic changes and personal preferences. In fact, consumers would be in charge of these systems rather than their being automatically governed. The overall level of comfort and energy use were affected differently by this decentralized strategy. The targeted temperature of the HVAC system might be modified by the adaptive building controller depending on the thermal feeling and comfort levels of a simulated person.
The study carried out by Ref. [
27] focused on achieving building energy savings and emission reduction through a dynamic programming algorithm. The dynamic programming algorithm achieved an impressive 35.1% reduction in energy consumption and emissions against the baseline scenario with a room temperature set at 20 °C. This indicates that the proposed control algorithm surpassed the baseline scenario significantly in terms of energy efficiency. Nevertheless, the algorithm produced an even more spectacular reduction in energy usage, reaching 47.49%, when imposing sacrifice in comfort (1–2 °C under the pleasant temperature 37% of the time).
The study conducted by Ref. [
28], proposed a multi-agent deep reinforcement learning method, MA-CWSC. The primary objective of this method was to enhance energy efficiency in the cooling water system. The experimental results demonstrated the effectiveness of the MA-CWSC method, and the energy-saving performance achieved by the proposed method demonstrates an improvement on the initial method of 11.1%. The energy-saving performance achieved by the proposed method was significantly superior to the rule-based control approach. Remarkably, the MA-CWSC method exhibited energy savings close to that of a model-based control method, with only a marginal 0.5% difference.
These significant energy savings are attributed, in part, to the shortcomings of standard HVAC system controls, which can result, among other things, from overly simplistic control algorithms or incorrectly configured setpoints [
59]. However, achieving a truly meaningful comparison necessitates aligning critical factors, including climate information, the thermal model of the building, and all variables impacting its thermal behavior. The scarcity of information and data required for a direct comparison under identical conditions restricts researchers from evaluating and contrasting results within the unique context of each study. Differences in building models, climatic data, control periods, and other parameters make it challenging to draw definitive conclusions beyond the scope of each individual research initiative.