Maintaining Indoor Comfort Research Articles

Glass serviceability limits: new evidence from human-centred studies

AbstractThe performance of the building envelope is crucial for minimizing operational carbon emissions of buildings and maintaining indoor comfort. Contemporary building envelopes, such as engineered glazed façades, achieve high performance levels but often add a significant amount of embodied carbon. There is therefore an incentive to reduce the thickness of the glass panels, but the minimum thickness possible is often not governed by strength or manufacturing limits but rather by the deflection (serviceability) limits. Despite objective criteria guiding serviceability limits, user acceptance of deformation remains unexplored, leading to conservative designs. This paper introduces a novel method for measuring user satisfaction with glass deformations, aiming to establish acceptance thresholds comparable to objective criteria. The study involves a novel experimental campaign to assess volunteers' levels of perception and acceptance of various glass deformations. The glass was deformed using a bespoke electro-pneumatic system at levels corresponding to below, above, and at the current serviceability limit. The results demonstrate the feasibility of measuring human responses to deformations in the glazing and provide essential data for setting serviceability limits. The experiments and corresponding user satisfaction feedback indicate that the current serviceability limit of L/50, may be relaxed, thereby presenting opportunities for material efficiency, such as the adoption of thinner glass in facades. The methodology effectively captures human responses, revealing that changes in reflection were the primary reason for the perception of movement; leading to a higher perception of glazing movement and a lower acceptance at night. Overall, participants felt safe regardless of their prior knowledge on glass properties, and providing this information to participants did not improve acceptance, which was already sufficiently high. The findings from this research fill an important knowledge gap in understanding user acceptance of glass deformations, crucial for comprehensive user satisfaction assessments and evidence-based reductions in glazing thickness.

Enhancing building energy efficiency: An integrated approach to predicting heating and cooling loads using machine learning and optimization algorithms

Predicting cooling and heating loads is essential for efficient building energy management in order to maintain indoor comfort. This study employs machine learning methods containing Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and a Dempster-Shafer Theory-based ensemble model using a dataset of 768 samples. 5-fold cross-validation selects 80 % of the dataset for training, with optimization algorithms like Coot, Dingo, and Sea-Horse Optimizers fine-tuning model applicability. Evaluation metrics such as R2, RMSE, n10-index, MARE, SI, WAPE, and NSE assess model performance. XGB outperforms SVR in heating and cooling load prediction. The XGSH model, combining XGB with the Sea-Horse Optimizer, obtains the highest performance, with an RMSE of 0.9729 and R2 of 99.17 % for heating load (HL) estimation. Similarly, for cooling load (CL) estimation, the XGSH model shows the highest R2 (99.36 %) and lowest RMSE (0.7723) during training. The results suggest practical usability for energy management and indoor comfort maintenance, benefiting engineers and decision-makers alike.

Heating performance of PCM radiant floor coupled with horizontal ground source heat pump for single-family house in cold zones

As the energy demand for space heating increases, the necessity for innovative and energy-efficient heating technologies has become more urgent. This study proposes and evaluates a heating system that integrates a phase change material radiant floor with a horizontal ground source heat pump (PCM floor-HGSHP, Case 1), intending to maintain indoor comfort temperatures and enhance energy efficiency through the utilization of renewable energy sources. Despite the considerable potential of PCM floors, their combination with HGSHP requires further research to fully unlock their benefits. The study involved simulating the PCM floor using a validated TRNSYS component and developing an advanced rule-based control strategy with time-of-use tariffs as a key input to optimize system operation. To elucidate and quantify the advantages of the PCM floor-HGSHP system and verify its applicability in cold regions of China, a single-family house was used as a case study building, four representative cities were selected, and two comparative simulation cases were defined, along with three key performance indicators. The results indicate that, compared with the radiant floor heating system with HGSHP (RFHS-HGSHP, Case 2), the incorporation of PCM in the floor of Case 1 improves the indoor temperature stability by 8.6 %, enhances the load flexibility by 18.1 %, and reduces the total operating costs by 13.8 %. Furthermore, relative to the PCM floor with air-to-water heat pump system (PCM floor-AWHP, Case 3), the proposed system which substitutes the AWHP with the HGSHP, extends the duration of indoor comfort temperatures by 9.6 % and decreases the system electricity consumption by 33.7 %. The proposed system demonstrates excellent thermal and energy performance across all selected cities, proving to be both technically feasible and significantly beneficial for maintaining indoor comfort temperatures and reducing operational costs. This study offers valuable insights and technical support for designing and optimizing residential heating systems in cold regions, thereby promoting the development and application of renewable energy sources.

Development of an IoT-Based Air Conditioning Meter for Real-Time Operational Status

Air conditioning systems have become a staple in households and businesses, particularly in regions with tropical climates, where maintaining indoor comfort is crucial. However, the widespread use of air conditioning units has led to a significant increase in energy consumption, contributing to higher electricity bills and environmental concerns, particularly in terms of carbon emissions. Traditional air conditioning units often operate without real-time monitoring, leading to inefficiencies and unnecessary energy use. In response to these challenges, there has been a growing interest in the development of smart, energy-efficient solutions that can monitor and control air conditioning systems remotely. The advent of the Internet of Things (IoT) has opened up new possibilities for integrating sensors and microcontrollers with cloud-based platforms, enabling real-time data collection and analysis. This research builds on these advancements by developing an IoT-based meter designed to monitor the operational status of air conditioning units, providing users with immediate feedback and enabling more efficient energy management.

Energy efficiency in modular emergency shelters: Impact of envelope finishings and shadowing

In emergencies, ensuring rapid access to temporary housing for displaced individuals is a cornerstone of effective disaster response. Managing the issue time-effectively is crucial for addressing the immediate physical needs of the population and fostering a sense of stability. Unfortunately, given the vast scale of emergency camps, the number of shelters to be installed is often very high, requiring rapid transport and assembly for the success of settlement procedures. Furthermore, emergencies frequently occur in environments with extreme outdoor climates, heightening the challenge of maintaining indoor comfort within shelters. The present study considers a modular shelter based on composite sandwich panels made of glass-fibre reinforced polymer (GFRP) filled with a polyurethane (PUR) foam thermal insulation core. Experimental tests and numerical analyses are used to quantify the thermal conductivity of the panels and the heat losses through the panels' connections, respectively. Dynamic energy simulations are implemented accounting for the aforementioned data and used to quantify the energy required to ensure users' thermal comfort via active heating and cooling systems. Further, the model is applied to evaluate the influence of envelope finishes and shading devices in reducing the shelter's energy demands in three extreme climates. Results indicate that the combination of shading and reflective finishes can lead to significant energy demand reductions, decreasing the need for thermal insulation and, therefore, reducing the overall weight of the shelter. Finally, it is observed that in climates where cooling demands are dominant, passive strategies can achieve energy savings that thermal insulation alone cannot attain.

Economic operation of an agent-based virtual storage aggregated residential electric-heating loads in multiple electricity markets

The increasing penetration of intermittent renewable energy sources leads to a higher demand for flexible balancing service in the power system. Consequently, demand-side management techniques, such as utilizing flexible resources from residential electric-heating loads, provide a promising solution. However, due to various uncertainties such as stochastic weather and market prices, it is challenging to develop an economically efficient strategy. Hence, this paper proposes an economic operation optimization approach for residential electric-heating loads to develop optimal bidding and scheduling strategy in the multi-stage market. First, the dynamic characteristics of aggregated electric-heating loads are modeled as the virtual energy storage systems (VESS) to quantify the flexibility potential arising from the combination of building thermal inertia, user tolerance and thermal energy storage (TES). Subsequently, the joint energy and balancing service market is introduced to exploit the operating flexibility of VESS. A multi-agent deep reinforcement learning (MADRL) optimization framework is proposed to address the multi-objective problem at different decision frequency, which involves achieving lower economic costs while maintaining indoor comfort. Finally, we combine the DRL with imitation learning (IL) by introducing the behavior cloning mechanism to mitigate training process instability. It reduces the computational costs and eliminates the need for redundant additional structure. Comparative simulation experiments demonstrate the efficiency of data-driven optimization approach, which provides an adaptive, economic cost-effective and general solution for multi-type aggregators to activate their flexibility in dynamic markets.

Model-Based Extreme Weather Data for Predicting the Performance of Buildings Entirely Conditioned by Ambient Energy

Abstract This study reports the development of extreme meteorological year (XMY) data for simulating buildings that are heated and cooled entirely by ambient energy in four climates varying in outdoor temperature and cloudiness. Electrification of conventional buildings is insufficient to meet climate goals since nearly half of U.S. electricity will still be produced from fossil fuels by 2050. Ambient-conditioned buildings depend on non-fossil sources such as the sun for heating and nighttime air or sky radiation for cooling. Such buildings are more susceptible to weather variability than conventional buildings, which simply use more auxiliary energy whenever weather conditions are challenging. On the other hand, ambient-conditioned buildings are more resilient to power outages so long as the design accounts for unusual weather during extreme years to consistently maintain indoor comfort. Ambient-conditioned buildings designed to remain comfortable with typical meteorological year (TMY2020) data produced up to over 1000 h per year of uncomfortable indoor temperature during the years (1998–2020) from which the TMY was derived. Parameters related to outdoor air temperature, sky temperature, and insolation were found to be unreliable for identifying the most challenging years. Rather, a whole-building model allowed the identification of the two most challenging years for heating and cooling, respectively. An XMY file concatenated from the most challenging summer and the most challenging winter provided a good match of indoor temperature predictions to those from the full individual years. This new XMY file facilitates the design of ambient-conditioned buildings for reliable indoor comfort.

Experimental Evaluation of a Solar-Powered Air Conditioner

Air conditioning is vital in maintaining indoor comfort and improving air quality, particularly in regions with high temperatures and humidity. However, the increasing demand for air conditioning has significant implications for energy consumption and the environment. Solar air conditioning can play a vital role in mitigating such impacts. This study presents an experimental setup that utilizes a solar photovoltaic system to power an air conditioning unit. The system is installed in a 36 m2-research lab at The University of Jordan, equipped with PV panels with a capacity of 2.67 kWp, a battery pack, a charge controller, an inverter charger, and an air conditioning unit. Two testing scenarios are conducted: an on-grid system connected to the electrical grid and an off-grid system operating independently. A clear sky day and a partially cloudy day were selected for each scenario in testing. The results were evaluated based on the solar fraction, energy consumption, and operational time fraction (OTF). The solar fraction was found to be higher during periods of higher demand and ambient temperature, and the average OTF was 70 % for all trials. In off-grid trials, the system had maintained operation solely on PV power during the studied load profile in the clear sky day. In contrast, limitations in charging power caused system shutdown in the partially cloudy trial. An economic analysis for the local payback period (PBP) was conducted based on the Hourly Analysis Program and PVsyst software simulation, which shows an attractive simple local PBP for on-grid and off-grid systems of 6.91 and 6.4 years, respectively.

Heating ventilation air‐conditioner system for multi‐regional commercial buildings based on deep reinforcement learning

AbstractIn an era of significant energy consumption by commercial building HVAC systems, this study introduces a Deep Reinforcement Learning (DRL) approach to optimize these systems in multi‐zone commercial buildings, targeting reduced energy usage and enhanced user comfort. The research begins with the development of an energy consumption model for multi‐zone HVAC systems, considering the complexity and uncertainty of system parameters. This model informs the creation of a novel DRL‐based optimization algorithm, which incorporates multi‐stage training and a multi‐agent attention mechanism, enhancing stability and scalability. Comparative analysis against traditional control methods shows the proposed algorithm's effectiveness in reducing energy consumption while maintaining indoor comfort. The study presents an innovative DRL strategy for energy management in commercial HVAC systems, offering substantial potential for sustainable practices in building management.

Enhancing self-consumption for decarbonization: An optimization strategy based on a calibrated building energy model

To face the challenge of climate change and achieve the decarbonization target set by the European Union, the current trend is to electrify building services, replacing the use of fossil fuels for renewable energy sources. The installation of grid-connected photovoltaic (PV) systems is becoming a popular strategy. However, the widespread application of PV solutions carries certain concerns about grid-network security and stability, since intermittent renewable energy excess pouring into the grid may exceed voltage limits. Therefore, an optimization of the consumption of a building's own PV production (self-consumption) to reduce the excess output is vital. The following paper performs a demand side optimization strategy of the building's thermostatic controllable loads (heating and cooling), which represent at least 50% of the total energy consumed by the building. The process is applied in a previously calibrated building energy model (BEM) that describes a fully operational building under a typical Mediterranean climate (Greece). The site contains a PV plant and a multi-split Variable Refrigerant Flow (VRF) system dedicated to maintain indoor comfort conditions. The technology used is simple, able to perform 15 minute time-step yearly optimizations while saving a large amount of computational time. It performs a bi-dimensional optimization of both: indoor thermal-zone set-points and ventilation air supply temperature. The optimization process performed is based on 2019 data gathered from European Project SABINA, resulting in a self-consumption improvement of 11.6% for summer scenario (reaching 69.16%) and 78.7% for winter (reaching 57.47%) in comparison to a non-optimized “business as usual” base model.

The other side

Building occupants are either used or compelled to live in overheated indoors in buildings, particularly in urban environments in the tropics. It doesn’t mean that they are acclimatized to overheated indoors. This scenario has become a normalcy in the current building design and operational practice. The fail- ure of building design to control heat transfer from outside to inside and remove indoor heat generat- ed by occupants and equipment in buildings to the outside is seen as a major reason for indoor over- heating. This normalcy is problematic for the occu- pants in particular and to the environment at large. It is problematic because of its negative effect on the indoor comfort level on one hand and associated emissions due to wasteful (or rather extensive) use of energy for cooling on the other hand. Overheating elevates the indoor thermal environment particu- larly the standard effective air temperature higher than the preferred thermal comfort range. Similarly, the dramatic effect of daylighting that could be gen- erated from architectural space is seen as least re- garded and not taken into the indoors meaningfully in today’s average building design and practice, thus largely depending on active systems. Lack of opti- mum balance in daylight, artificial or mixed mode visual environments demands more energy either to maintain indoor visibility or to combat glare and heat stress associated with tropical daylighting, or both. This overall practice uses extensive amounts of ac- tive energy to maintain indoor comfort (both thermal and visual) contributing to emissions and weakening the demand side efficiency of operational stage.

Optimizing Window Configuration Counterbalancing Energy Saving and Indoor Visual Comfort for Sydney Dwellings

Building penetrations are the most-potent elements providing daylight and moderating the lighting energy consumption and affecting indoor comfort and consequent energy usage. In a semi-tropical climate with a green environment such as Sydney, there is a radical demand to extend windows providing views. This research aims to optimize sunlight admission and maintain indoor comfort while minimizing energy consumption. The method for investigation is to simulate a multiobjective optimization using NSGA-II considering visual and thermal comfort along with energy usage and view of the outside. A combination of human and machine assessments responding to manual and microcontroller-operated indoor validating simulation improves the generalizability. The solutions were assessed for local codes compliance and double-checked against statistical sky conditions. Regarding north, a window-to-wall ratio of 10.7–20% delivers an optimum daylight metric, yielding a 12.16% decrease in energy use intensity. For an east-facing window, altering 26.4% of WWR decreases 2% in lighting energy and a provides a drastic change in visual comfort. Regarding west, changing WWR by about 51% brings about a 50% saving in lighting but no change in other energy loads. Regarding south, when window length is limited to 39% envelope width, it delivers the optimum energy consumption. This study covers visual and thermal comfort together with energy usage and view of the outside, which has not been investigated for southern hemisphere dwellings. A combined simulation and field measurement of human and machine assessment justifies the solutions.

Towards comfortable and cost-effective indoor temperature management in smart homes: A deep reinforcement learning method combined with future information

The extension of in-home time has boosted people's increase demand for comfortable indoor temperatures. This paper aims to propose a smart home-based temperature control framework based on deep learning and reinforcement learning to automatically control indoor temperature. The heat pump power and the ventilation system volume could be adjusted by the proposed framework to minimize costs while maintaining indoor comfort. The uncertainty of future information and the requirement for continuous control make it challenging to build an intelligent temperature control system. Simultaneously, the human body is sensitive to different temperatures. Therefore, the selection of appropriate evaluation criteria for people’s thermal comfort is essential. To address this problem, the Predicted Mean Vote (PMV) criterion is applied to scientifically evaluate human thermal comfort. The indoor temperature control problem is transformed into a Markov decision process. A deep learning framework (Proximal Policy Optimization PPO) based on continuous control actions is proposed to solve this problem. Combined with the deep learning method Long Short-Term Memory (LSTM), future state information is applied to improve the control stability. The proposed framework performs well under different conditions in summer and winter. The proposed framework performs well under different conditions in summer and winter. It is compared by the discrete action control method Deep Q Network and the PPO algorithm, which does not contain future state information. As a result, the proposed framework achieves 24.29% and 23.63% cost savings in winter and summer, respectively.

Design for energy flexibility in smart buildings through solar based and thermal storage systems: Modelling, simulation and control for the system optimization

The present study investigates the use and implementation of energy efficient measures and strategies for building applications, toward the Nearly Zero Energy Buildings target. Specifically, objective of the study is to implement building integrated photovoltaic thermal devices coupled with a phase change materials heat exchanger acting as an active thermal storage building component, with the aim to add flexibility to the building while still maintaining indoor comfort conditions. To show the potentials of the novel configuration proposed in this paper, a multi-zone grey-box model is developed and validated to capture the thermal dynamics of a building, and a control strategy applied to the whole system is developed for energy management purpose. The whole simulation model, including thermophysical properties of the building-system and the control features, is implemented in a MATLAB environment. To assess the model and application potentials toward the optimal design and operation of the proposed system for energy efficiency and flexibility goals, a suitable case study analysis is conducted. Thus, a sensitivity analysis, using an evolutionary algorithm, is performed by considering economic and energy objective functions which focuses on the reduction of the building energy demand, load variability and economic aspects. In this regard, the optimal design configuration is underlined in a way that the operation of the components can be maximized to provide flexibility to the building: in average working conditions one single layer of PCM can provide around 186.3 Wh/K per unit of temperature and width. A rule-based management strategy is proposed to prove the possibility to shift and shave the energy peaks during high energy request periods, demand response events. Finally, by considering an approximate economic calculation, the simple payback, taking into account only the positive effects on the winter management, is around 13.5 years.

Trade-off decisions in a novel deep reinforcement learning for energy savings in HVAC systems

This paper presents Model-based Reinforcement Learning (MB-RL) techniques to control the indoor air temperature, and CO2 concentration level, and minimize the energy consumption of the heating, ventilating, and air conditioning (HVAC) systems, simultaneously. For this purpose, a trade-off is made between maintaining indoor comfort levels and minimizing energy consumption. The control of the HVAC system is performed using the Deterministic Policy RL (DP-RL) method. Moreover, the nonlinear autoregressive exogenous neural network (NARX-NN) is employed as an approximation function with DP-RL method to provide a hybrid DP-NARX-RL controller. By applying the DP-RL and DP-NARX-RL controllers to the HVAC system of a typical building, parameters such as the indoor comfort levels, the electrical power, and energy consumed, and the energy costs at various pricing schemes are evaluated for two case studies. In both cases, the results show the better performance of DP-NARX-RL compared to DP-RL, RL, and PID controllers.

Towards an All-Solid-State Electrochromic Device: A Review of Solid-State Electrolytes and the Way Forward.

In order to curb high electricity usage, especially in commercial buildings, smart windows, also known as “switchable” or “smart” glasses, have attracted a significant amount of attention in an effort to achieve energy savings in eco-friendly buildings and transportation systems. Smart windows save energy by regulating the input of solar heat and light and hence cutting down air-conditioning expenses, while maintaining indoor comfort. This is achieved by electrochromism, which is defined as the reversible colour change in electrochromic (EC) materials from transparent to dark blue and vice versa under a small applied voltage. Recent state-of-the-art electrochromic devices (ECD) adopt liquid-based electrolytes as the main source of energy for basic operations. While this has resulted in much success in ECDs as reported in past studies, there remain several drawbacks to this aspect, such as liquid electrolyte leakage and evaporation, not to mention safety concerns related to the harmful nature of electrolyte materials. This paper aims to review the recent advances in various solid electrolytes that are potential solutions to the mentioned problems.

Sky Radiation Decreases Thermal Mass Requirements to Achieve 100% Ambient Cooling in Hot US Climates

Abstract Ambient House is a building that maintains indoor temperature within a comfortable range by controlling gains and losses from ambient sources and utilizing thermal mass to moderate temperature changes when the sources are unavailable. Previously, necessary building characteristics were determined for passive solar as the heating source and ventilation as the cooling source in 11 US climate zones. (Sharp, M.K., 2012, “Indoor Comfort Achieved Exclusively from Ambient Sources Across US Climates,” ASME J. Sol. Energy Eng. 143, (6), p. 061005.) It was noted that in hot climates, such as Phoenix, AZ, there are long periods during which outdoor temperature is too warm for cooling, necessitating large thermal mass to avoid indoor overheating. In this article, thermal mass requirements are compared between sky radiation and nighttime ventilation cooling in all 16 US climate zones, including marine subzones 3C and 4C and very cold and subarctic zones 7 and 8. It is shown that sky radiation provides shorter intervals of cooling unavailability and allows much smaller thermal mass to achieve year-round indoor comfort in the hot climates of Las Vegas, Miami, New Orleans, and Phoenix, while it provides no significant benefits in cool climates, where thermal mass is dictated more by the need to slow the decrease in indoor temperature during cloudy periods in the winter. In Fairbanks, AK (zone 8), in particular, the lack of significant solar gains for almost 3 months during the winter requires large thermal mass to maintain indoor comfort. Minimal thermal mass is needed to meet the small summer cooling demand, and both sky and ventilation cooling are sufficient.

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

Climatology and Trends of Heat Index, Human Discomfort Index, and Energy Per Capita for CONUS and Meso-America

Abstract Air temperature and humidity affect human comfort and health in warm and humid climates, and consequently energy demands from buildings to maintain indoor comfort levels. An effective way to measure the overall effect of temperature and humidity on human comfort (or discomfort) is using the heat index (HI), a measure of how people “feel” when exposed to warm and humid environments. This research aims to investigate the spatial and temporal changes and trends of HI and associated energy per capita (EPC) to maintain human comfort in the Continental US (CONUS) and the Meso-America (the Caribbean, and Northern Regions of South America). Hourly air temperature and relative humidity datasets were collected from three sources: the National Center for Environmental Prediction, the North American Regional Reanalysis (NARR), and surface weather stations for a period of 30 years: 1990–2019. The algorithm used for the HI is similar to the one used by the US National Weather Service whereas the EPC is based on the ventilation requirements per person. Results for HI and EPC climatology for the summer season indicate the largest increasing values in Southeast US, followed by the Greater Antilles, and then the Lesser Antilles. Results of the analysis depict positive EPC rate of 2 kW h per year for the summer season for the Southern Greater Antilles. These trends using NARR data were found to be statistically significant and correlate very well with selected weather stations. The actual trends of electricity consumption per person per year for the CONUS suggest, in general, a correlation with increasing maximum HI.

Thermal performance and comfort assessment of U-shape and helical shape earth-air heat exchanger in India

Earth Air Tunnel Heat Exchanger (EATHE) systems are not so much popular in India because of restricted accessibility of ground space in majority of residential and commercial sector. In the present article, thermal performance of newly designed helical shaped EATHE system has been compared with conventional U–shaped EATHE system by keeping similar geometrical and operational configuration for the summer (April–June) and winter (December–February), respectively. In this regard, therefore, numerical model of both EATHE systems have been designed and experimentally validated. The thermal performance of both EATHE systems has been compared in terms of outlet air temperature and effectiveness. The present numerical study suggested that the newly designed helical EATHE system exhibits better thermal performance with lower ground requirement as compared to conventional U-shaped EATHE system. To evaluate the thermal comfort potential of EATHE system in office buildings of India, composite climate specific adaptive thermal comfort boundaries has been used. The detailed thermal performance behaviour of the EATHE system suggests that an additional 80% of the time; ambient air conditions became thermally comfortable during peak summer season. In addition, analysis of the results revealed that EATHE system has more potential during heating operation mode to maintain indoor comfort in winter season of composite climate in India.