HVAC Energy Research Articles

Prediction of building HVAC energy consumption based on least squares support vector machines

Air conditioning, as an essential appliance in daily life, has the function of ensuring comfortable room temperature, but it is also accompanied by a large amount of power consumption. Consequently, the study suggests an energy consumption prediction model based on improved genetic algorithm—least squares support vector machine—to accurately predict the energy consumption of building heating, ventilation, and air conditioning. This model uses the improved genetic algorithm for regularization parameter and kernel parameter optimization to prevent overfitting and underfitting issues. According to the testing results, the least squares support vector machine, an upgraded genetic algorithm, may accomplish convergence faster than other algorithms, taking only 0.2 milliseconds to finish. In addition, the average relative error of the improved genetic algorithm- least squares support vector machine did not exceed 0.6%. In the energy consumption prediction for the whole year of 2022, the average error of the improved genetic algorithm-least squares support vector machine was only 2.0 × 106 kWh, and the prediction accuracy could reach up to 97.2%. The above outcomes revealed that the energy consumption prediction model can accurately predict the air conditioning energy consumption, which provides a strong support for the control and optimization of the air conditioning system.

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

Protocols for planning micro-zones to facilitate occupant-centric control (OCC) to reduce HVAC energy consumption in Indian open-plan offices

Protocols for planning micro-zones to facilitate occupant-centric control (OCC) to reduce HVAC energy consumption in Indian open-plan offices

Predictive modelling of cooling consumption in nursing homes using artificial neural networks: Implications for energy efficiency and thermal comfort

The growing need for cooling within the built environment, propelled by climate change and the expansion of nursing homes due to the increase in life expectancy, highlights the urgency of implementing energy-efficient strategies in buildings occupied by older populations. As of today, there remains a need for comprehensive research into the influence of indoor and outdoor conditions, building, operational, and occupant characteristics, on energy consumption specifically for nursing homes. This study develops a systemic artificial neural network-based model with a multi-layer perceptron architecture to assess HVAC energy implications during the cooling season for older populations. Using monitored data from eight nursing homes, the model includes cooling area, construction age, outdoor and indoor temperatures, and outdoor relative humidity as inputs, and cooling consumption as the output. Results show excellent predictive capability (R2=0.95), with mean error of −0.5 kWh, root mean squared error of 13.7 kWh, mean absolute error of 10.2 kWh, and relative error of 0.051. These outcomes are better compared to linear models (R2≈0.65) under the same data set. Adjusting operative temperatures adaptively can significantly enhance resident comfort and achieve up to 23.4 % energy savings, particularly in hotter, drier climates. These findings are of paramount importance for effective energy management in buildings.

BEForeGAN: An image-based deep generative approach for day-ahead forecasting of building HVAC energy consumption

This study presents a pioneering approach in building energy forecasting by introducing a novel reformulation framework that transforms the forecasting task into an image inpainting problem. Based upon the fundamental notion that “forecasting is about generating data of the future”, we propose BEForeGAN, an innovative deep generative approach for day-ahead Building HVAC Energy consumption Forecasting based on multi-channel conditional Generative Adversarial Networks (GANs) with U-Net generators. Our method is evaluated using 96,360 hourly HVAC energy consumption records from 11 buildings, demonstrating significant accuracy improvements of 17%∼76% and a substantial variability reduction of 3%∼96% compared to a suite of conventional and deep learning benchmark models across individual-building and zero-shot cross-building forecasting tasks. Notably, BEForeGAN exhibits robustness to noisy inputs, with an increase below 3% in Coefficient of Variation of Root Mean Square Error (CV-RMSE) for each 10% noise increment. This study addresses critical gaps in existing literature by showcasing the untapped potential of GANs as standalone forecasters, advocating for further exploration of two-dimensional (2D) GAN-based methods in building energy forecasting, and emphasising the need for more studies focusing on cross-building forecasting tasks. In conclusion, our findings underscore the transformative impact of GANs in revolutionising building energy forecasting practices, paving the way for enhanced energy-efficient building management and beyond.

A comparison of occupancy-sensing and energy-saving performance: CO2 sensors versus fisheye cameras

A number of occupancy-sensing methods have been proposed to date with the goal of saving HVAC energy in commercial spaces by means of demand-controlled ventilation. Currently, CO2 measurement is the most common approach deployed in practice. However, recently a number of other approaches have been proposed, such as using surveillance cameras, depth sensors, thermal imagers, etc. In this paper, we compare the occupancy-sensing performance of high-accuracy commercial CO2 sensors and overhead fisheye cameras supported by deep-learning algorithms. Our experiments are conducted over 3 days in a large university classroom with highly-dynamic occupancy. First, we assess the impact of parameter selection and sensor placement on CO2-to-occupancy conversion accuracy. Then, for the best-performing setup, we compare this accuracy against that of fisheye cameras. Subsequently, we estimate the potential energy savings offered by both systems. Our results show that overhead fisheye cameras produce on average a 20% lower occupancy-estimation error than CO2 sensors in steady-state periods, and almost 70% lower error in transient periods across 4 performance metrics used in this work. In terms of potential energy savings, fisheye cameras offer on average 6 percentage points of additional savings compared to CO2 sensors. Given their relatively low cost and implementation simplicity, CO2 sensors are an attractive approach to saving HVAC energy, however their occupancy-estimation accuracy is highly sensitive to parameter selection that is challenging in practice. Occupancy sensing based on fisheye cameras is not subject to similar parameterization sensitivity and in addition to outperforming CO2 sensors allows precise localization of occupants in a space thus potentially enabling additional space-management and safety/security services, that CO2 sensors cannot offer.

Load prediction and energy efficiency improvement of steelmaking waste heat centralized heating systems under mild climate in China

HVAC energy consumption accounts for more than 50 % of the total building energy consumption globally and using industrial waste heat has huge potential for energy savings in central heating systems. Under the background of the use of renewable energy and the gradual rise of central heating in mild climate areas in China, it is worth paying attention to the problem of excessive energy consumption caused by the inaccurate load prediction of central heating systems when the ambient temperature changes. L City, Guizhou Province, is the first city with central heating in southern China. The temperature variation during winter in L City can manifest the climatic characteristics of a mild region. Therefore, based on the operational data of the steel waste heat central heating system in L City, Guizhou Province, China, during the heating season of 2022–2023, this study discusses the load prediction and energy-saving optimization strategy of the central heating system in a mild climate. SketchUp was used to conduct 3D modeling of the actual buildings in the heating area and TRNSYS was imported to simulate the system load. The difference between the heating load forecast and the actual operating data was analyzed in detail by using the calculation results. Based on the simulation results, the waste heat utilization strategy was adjusted and the influence of different heating schemes on the pump energy consumption under different load demands was discussed. The results reveal that based on the temperature change rule, the accurate load prediction can lead to a 28.64 % saving of the system energy. Concerning system energy consumption, by adjusting the water flow and choosing the large and small pump system, the energy saving amounts to 39.48 % compared with the traditional single-pump system. Meanwhile, in the transition season when the heating demand is less than 20 MW, the adjusted heating scheme can achieve a 38.5 % saving based on the constant flow scheme and a 5.4 % saving based on the constant temperature difference scheme. This article provides a scientific energy-saving solution for heating in mild climates using industrial waste heat, not only reducing energy consumption but also providing an important reference for the energy-saving renovation of heating systems in similar areas.

Implementing an Adaptive Climate Control Strategy: Collection Monitoring and Sustainability Outcomes

ABSTRACT In 2022, the authors presented the first stage of implementing a revised climate control strategy at the National Gallery of Victoria, monitoring collection object response in collaboration with the Getty Conservation Institute using acoustic emissions (AE) monitoring. The research and education, motivating factors, and interdepartmental alignment required for adopting the Bizot Green Protocol (BGP) were discussed, as well as the experimental design and set-up of the AE system on a large Flemish altarpiece in an active gallery. This second and concluding paper presents the results of the AE monitoring study along with energy used by the HVAC system for the gallery housing the AE monitoring study, during the transition from traditional environmental parameters through to broadened parameters as defined in the BGP. The project highlights the importance of understanding individual gallery and building behaviour as well as the HVAC system in place. In the case of the gallery housing the AE study, the implementation of BGP parameters resulted in smaller environmental fluctuations than expected. That HVAC energy efficiency could be improved with a relatively low impact on the gallery environment was an unexpected but welcome outcome. This case study describes some common challenges faced when considering such a change operationally, including resourcing and communication needs. Identifying points of resistance, framing problems clearly, and identifying common goals were essential to making progress. In this case, sustainability has compelled many stakeholders into alignment and enabled a shift in institutional culture.

A Flexible Electrochromic Device for All-Season Thermal Regulation on Curved Transparent Building Envelopes.

As one of the least energy-efficient components in buildings, transparent building envelopes are responsible for approximately 60% of the total energy losses. Although controlling solar transmittance through electrochromic modulation is an effective method for temperature management in these structures, a dynamic control strategy for solar light on curved transparent building envelopes is still lacking. In this study, we introduce a dual-mode flexible electrochromic device based on reversible silver deposition for curved transparent building envelopes. The device operates by reversibly depositing and dissolving silver on a flexible polyethylene terephthalate-indium tin oxide (PET-ITO) substrate, controlled through the application and removal of pulsed voltage. This mechanism enables rapid switching between radiative cooling and solar heating modes, leading to modulation of solar reflectance from 89.1% to 15.7% and solar transmittance from 0.02% to 72.9%. Under approximately 700 W/m2 of solar irradiance, the device achieves an average temperature reduction of 1.6 °C (with a maximum reduction of 4.3 °C) compared to ambient temperature in radiative cooling mode. In solar heating mode, the device achieves an average temperature increase of 17.1 °C (with a maximum increment of 23.7 °C) compared to ambient temperature. Simulation results show that the dual-mode flexible electrochromic device could offer all-season thermal regulation for curved transparent building envelopes and achieve a maximum of over 50% annual HVAC energy savings.

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC − heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2 %, recall of 89.7 %, precision of 88.5 % and F1-score of 89.2 %. These results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

Solar Window Innovations: Enhancing Building Performance through Advanced Technologies

Building-integrated photovoltaic (BIPV) glazing systems with intelligent window technologies enhance building energy efficiency by generating electricity and managing daylighting. This study explores advanced BIPV glazing, focusing on building-integrated concentrating photovoltaic (BICPV) systems. BICPV integrates concentrating optics, such as holographic films, luminescent solar concentrators (LSC), Fresnel lenses, and compound parabolic concentrators (CPCs), with photovoltaic cells. Notable results include achieving 17.9% electrical efficiency using cylindrical holographic optical elements and crystalline silicon cells at a 3.5× concentration ratio. Dielectric CPCs showed 97.7% angular acceptance efficiency in simulations and 94.4% experimentally, increasing short-circuit current and maximum power by 87.0% and 96.6%, respectively, across 0° to 85° incidence angles. Thermochromic hydrogels and thermotropic smart glazing systems demonstrated significant HVAC energy savings. Large-area 1 m2 PNIPAm-based thermotropic window outperformed conventional double glazing in Singapore. The thermotropic parallel slat transparent insulation material (TT PS-TIM) improved energy efficiency by up to 21.5% compared to double glazing in climates like London and Rome. Emerging dynamic glazing technologies combine BIPV with smart functions, balancing transparency and efficiency. Photothermally controlled methylammonium lead iodide PV windows achieved 68% visible light transmission, 11.3% power conversion efficiency, and quick switching in under 3 min. Polymer-dispersed liquid crystal smart windows provided 41–68% visible transmission with self-powered operation.

Investigating enhanced thermal comfort and energy efficiency through strategized airflow in Micro-Zonal Occupant-Centric Control (MZOCC)

The potential of Micro-Zonal Occupant Centric Control (MZOCC) to save HVAC energy consumption in medium sized open-plan offices has been established in literature. This is done by virtually dividing a thermal zone into micro-zones and allowing independent diffuser-level control in each micro-zone when the micro-zone is occupied. In continuation, there is a need to ensure that thermal comfort is achieved within micro-zones under such localised control. This is important because several forms of discomfort such as draft, horizontal thermal asymmetry and vertical thermal gradients may arise in MZOCC. Further, the effect of MZOCC airflow control strategies, such as setback flow and setback temperature, which were proposed in previous studies on thermal comfort of occupants needs to be evaluated. In an attempt to address these research gaps, this study explores thermal comfort for different scenarios of diffuser arrangements, airflow control strategies and occupancy conditions and also explores the effect of varying supply velocities used in setback flow and setpoint temperatures used as setback temperatures on air distribution and thermal comfort.Results show that low PMV values, increased draft discomfort and heavy thermal gradients are observed for certain scenarios of MZOCC. Tuning airflow control by choosing appropriate setback flow or setback temperature helps in restoring thermal comfort. As different forms of discomfort are observed in different cases, it is important to plan micro-zones and control airflow, such that, MZOCC does not violate thermal comfort criteria while optimising energy efficiency. This study gives a framework for selecting appropriate airflow control strategies for a selected diffuser arrangement for a given occupancy condition. Comfort-based MZOCC that uses appropriate airflow control strategy saves about 60% energy when the zone is half occupied and about 85% energy when the zone is quarter occupied. Results also show that, improving the diffuser arrangement improves energy savings and increases the options of airflow control strategies that can be used. Also, different airflow control strategies behave differently for each diffuser arrangement and has to be chosen separately for given diffuser setting and occupancy condition.

Comparasion of HVAC Energy Consumption Prediction in an Academic Building using LSTM and DNN

Energy consumption information is a collection of information obtained from datasets that is useful for making decisions for energy conservation. In this paper, we proposed a modern approach based on LSTM and DNN. While many researchers have employed these methods for predicting energy consumption, this paper seeks to compare their efficacy to determine which is superior. The comparative analysis in question employs accuracy metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. Furthermore, the accuracy metric outcomes indicate that the LSTM method surpasses the DNN approach in terms of the R-squared (R2) value, with respective scores of 0.931 and 0.782. Meanwhile, for other accuracy metrics, the DNN method outperforms LSTM. Nevertheless, the performance of the two proposed methods is excellent, as evidenced by the R-squared (R2) value exceeding 0.75, which aligns with modeling standards observed in numerous research studies.

Investigation of the indoor CO2 removal efficiency and fresh air energy savings of living walls in office spaces

Elevated indoor CO2 levels might have adverse effects on human health. However, conventional physical and chemical methods for indoor CO2 removal face the challenges of high energy consumption, low efficiency at low CO2 concentrations, and high costs. Furthermore, the introduction of outdoor air to lower indoor CO2 concentrations results in significant HVAC energy consumption. Aligning with office hours and the natural light cycle, the utilization of photosynthesis in living walls (LWs) offers an energy-efficient and sustainable solution for the mitigation of high CO2 levels in office spaces. This study experimentally investigates the impacts of the carbon fixation pathways, light flux density, and substrate moisture content on the CO2 removal rate of LWs in two identical rooms. The findings provide practical suggestions for plant species selection, lighting design, and irrigation management in office spaces to enhance the CO2 removal efficiency of LWs. Additionally, the fresh air energy-saving effects of LWs under different scenarios are accurately simulated in EnergyPlus. The results demonstrate that choosing C3 plants over CAM plants in LWs yields higher CO2 removal efficiency. The activation of artificial lighting is recommended when the surface light flux density of LWs drops below the light compensation point (LCP), ensuring favorable conditions for growth. In a 30-m2 office room accommodating two and three occupants, LWs can respectively reduce the demand for fresh air by 13.9 %–38.5 % and decrease fresh air energy consumption by 11.2 %–28.2 %.

The potential of Earth-to-Air Heat Exchangers for reducing energy demand in Spanish dwellings

An earth-air heat exchanger (EAHX), also known as Provençal well or Canadian well, is a system for cooling and heating buildings using the ground as a heat sink/source. This study is dealing with earth-air heat exchanger (EAHX) implementation for Spanish dwellings. The objective is to assess the potential of this solution for HVAC energy savings and greenhouse gas emissions reductions. A numerical study is conducted to assess the thermal behavior of EAHX using the transient simulation tool TRNSYS. The simulations are carried out for typical Spanish single family detached home, taken into account three different climate regions. Detailed results are presented concerning EAHX thermal behavior and its benefits in terms of energy savings and anti pollution effects for fresh air pre-heating and pre-cooling within mechanical ventilation system of the house.

Numerical analysis, design and application of a decentralized ventilation system with a heat recovery unit adapting to Nordic climates: A case study in Norway

This research investigated the energy efficiency of decentralized ventilation (DV) systems in cold weather conditions in comparison to centralized ventilation (CV) systems, focusing on three locations in Norway. The study found that DV systems with higher heat recovery unit (HRU) efficiency consumed less thermal and fan energy due to shorter air distribution pathways, utilizing a hybrid natural ventilation strategy in mild climates, and simpler space zoning for ventilation. This study proposes to determine a suitable ventilation system for adapting to cold climate. These methodologies are presented in three major parts: 1) ventilation energy demands of DV and CV systems in Norway, 2) performance of the heat recovery unit in the DV system, and 3) numerical analysis of the total ventilation energy demand of the DV system in cold climates using occupant ratio and existing measured data.The results showed specific time periods when it is ideal to use a fan-assisted NV system in cold climates, and in local conditions, 4–20% per year could benefit from a fan-assisted NV system using DV technology. A DV system with 0.7 HRU efficiency can save up to 14.5% of total HVAC energy compared to a CV system. The HRU heating efficiency played a crucial role in thermal ventilation energy demands in cold weather, while the cooling efficiency had no significant effect on energy demands. However, if a hydronic heating and cooling coil is added to the DV system, it consumes more than 9.08% of energy than using a pump. The energy-saving performance of the DV system decreases significantly if its HRU efficiency is below 50%, and there is no real advantage of using the DV system over the CV system in such cases. Nonetheless, the DV system can be utilized in various working conditions to conserve energy, such as different occupancy ratios, individual room space zoning, and indoor air pollution rates.

Microwave Doppler Radar Occupancy Sensing for HVAC Energy Savings

Microwave Doppler Radar Occupancy Sensing for HVAC Energy Savings

Thermochromic Smart Windows with Ultra-High Solar Modulation and Ultra-Fast Responsive Speed Based on Solid–Liquid Switchable Hydrogels

Thermo-responsive hydrogels can dynamically modulate incident light, providing a broad prospect for development of smart windows, which are of pivotal importance for energy conservation in buildings. However, these hydrogels normally exhibit slow response speed and tend to contract over extended phase transition, compromising structural integrity of smart windows. In this study, a solid–liquid switchable thermochromic hydrogel, denoted as SL-PNIPAm, was synthesized by cross-linking PNIPAm with AMEO through dynamic imine bonds. Due to its distinctive solid–liquid transformation characteristics, SL-PNIPAm demonstrates rapid response time (within 5 s) and retains structural integrity without undergoing shrinkage during heating/cooling and freezing/thawing cycles. SL-PNIPAm can also be encapsulated within 2 glass panels to prepare smart windows, which showed extraordinary luminous transmittance ( T lum = 96.8%) and solar modulation ability (Δ T solar = 89.7%) and effectively reduced the indoor temperature (22 °C) in a simulated indoor experiment. Energy consumption simulation investigations are performed in diverse cities. The results reveal that SLW is capable of achieving a remarkable 54% reduction of HVAC energy consumption, leading to substantial decrease in CO 2 emissions by up to 40 kg m −2 annually. This work develops a new hydrogel system with outstanding durability for smart windows and will promote the development and renovation of thermochromic smart windows.

Enhancing HVAC energy management through multi-zone occupant-centric approach: A multi-agent deep reinforcement learning solution

Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants.

Correlation of AC energy consumption and passive thermal comfort with floor levels in tropical high-rise residential dwellings

The relationship between thermal comfort, building floor levels, and HVAC energy consumption in tropical high-rise residential buildings remains unclear. These buildings face heavy cooling demands due to their immense height and volume, as well as hot-humid weather found in the tropics. Architects and building developers understand that an increase in a building's floor count increases its energy requirements, but the impact on occupant thermal requirements and AC energy consumption needs further investigation. This study aimed to evaluate the relationship between passive thermal comfort, AC energy consumption, and floor levels in tropical high-rise residential buildings through survey and experimental methods. The results showed a significant relationship between thermal sensation votes and building floor levels, as well as AC energy consumption and floor levels. The study also found a strong correlation between thermal sensation votes and predicted mean votes, AC energy use, and predicted people dissatisfied. The optimal cut-off floors for thermal dissatisfaction according to (TSV) are 22nd floor and 8th floor in summer and monsoon seasons respectively. With a 90% sensitivity and 92% specificity. This research highlights the importance of addressing thermal comfort in high-rise buildings to improve energy efficiency and thermal comfort in tropical climates.