Indoor Temperature Research Articles

Bio-based composite phase change material utilizing wood fiber and coconut oil for thermal management in building envelopes

Bio-based materials are attracting substantial attention for their eco-friendly attributes within the construction field, where they facilitate energy saving and emissions reduction. However, the performance of it, such as thermal properties, needs to be improved. Fully utilizing bio-based materials, we developed a novel composite phase change material (PCM), A-WF/CO/h-BN, with high thermal storage properties designed for application in building envelopes. It incorporates coconut oil (CO) as the PCM featuring an optimal phase change temperature. Hexagonal boron nitride (h-BN) was chosen to enhance the thermal conductivity. Acetylated wood fiber (A-WF) was employed as the supporting material. The prepared A-WF/CO/h-BN sample exhibits excellent and rapid heat storage and exothermic capabilities. In the experiment of thermal performance evaluation, the cold end of the A-WF/CO/h-BN sample is cooler than that of the A-WF when both hot ends are heated. Results indicate that the proposed A-WF/CO/h-BN sample can effectively delay the heat transfer and inhibit the temperature rise and fall. For these superior properties, when the A-WF/CO/h-BN sample is applied in building envelopes, it can minimize the heat transfer from the envelope to the interior and inhibit indoor temperature fluctuation. Thus, it can reduce building energy consumption and carbon emissions while achieving indoor thermal comfort.

Evaluating different CFD surrogate modelling approaches for fast and accurate indoor environment simulation

Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.

Indoor temperature and CO[formula omitted] in South African primary school classrooms: Inspecting brick, container, and prefab structures

In the resource-constrained education sector in South Africa, infrastructure deemed temporary or backup elsewhere is used as permanent classrooms, mainly in areas of low income. Temperature and CO2 levels, substantial determinants of air quality, have not been studied across different classroom types, despite their direct impact on children’s cognitive performance and comfort. We measure CO2 and temperature every 11 min in 24 classrooms at two schools in Stellenbosch, South Africa. Container classrooms with and without insulation, mobile (prefabricated) classrooms, and brick classrooms of different configurations are included. Measurements were concurrently sampled over twelve months, across multiple seasons with relevant metadata, including ambient weather conditions. We analyse temperature and CO2 concentrations for classroom types and classroom categories and compare school days with non-school days. The results show that temporary classrooms (container and mobile) have substantially worse thermal environments, even when air conditioning is available. The CO2 concentrations in container classrooms are substantially worse, whereas mobile classrooms have better conditions comparable to brick classrooms. We conclude that container classrooms have the worst temperatures and ventilation, prefabricated classrooms have acceptable ventilation but poor thermal qualities, and brick classrooms have the best thermal environment and ventilation.

How to investigate differences in indoor environmental comfort and adaptation? Mixed methods research with patients

ABSTRACT Ensuring hospitalized patients’ comfort requires understanding differences in comfort and adaptation to discomfort, as well as the role of the built environment therein. Shortcomings in this understanding result from limitations of existing research methods. Therefore the potential of a mixed methods research design to investigate when and why experiences of and adaption to (dis)comfort differ among hospitalized patients was explored. A convergent mixed methods case study was conducted in two hospital wards of a Belgian hospital. Qualitative data included interviews with 19 patients about their experiences and information about the patients at the two wards during the fieldwork; quantitative data included sensor measurements of indoor environmental quality parameters (i.e. illuminance and indoor temperature), a questionnaire among 238 patients about their assessment of the indoor conditions, and simulations of these conditions. Joint displays allowed comparing situations in which characteristics related to the patients or their setting differed in an integrated way. The displays provide an intermediate step between purely qualitative descriptions of comfort and adaptation and purely quantitative models that predict both. Scenarios that the joint displays allow identifying inform about characteristics that could improve these models, and about roles of the built environment in experiences.

Evaluation of CO2 and TVOC concentration in a school in Palma during the month of June

ABSTRACT In the context of a warmer climate, one of the population sectors most affected are children. Therefore, it is important to consider the study of school buildings since children invest there about 30% of their time. In schools, human activities (CO2) are the main sources of pollution, along with the emissions of construction materials and cleaning products (TVOC). Additionally, reaching good indoor air quality should not be to the detriment of adequate thermal comfort. This work aims to make a comparison between different classrooms of a school in Palma (Mallorca, Spain) by assessing the values of relative humidity, temperature, TVOC and CO2 concentration during the month of June, the warmest month. This study aims to contribute to the field with the achieved outcomes. It is observed that CO2 increases until it exceeds the limit levels, established by current policies, up to 54% of the time during school time. TVOC concentration also exceeds the limit, up to 65% of the time. On the other hand, indoor temperature is above the comfort range between 75 and 100% of the time, being higher than the outdoor temperature. The relative humidity is mainly within comfortable values.

Optimizing heat pump unit selection for recirculating aquaculture workshops through computational fluid dynamics.

The selection of water temperature regulation equipment plays a crucial role in the design of workshops. At present, the choice of water temperature control equipment is usually based on the volume of the fish pond and thermal parameter calculation, combined with aquaculture experience. Empirical formulas only work in specific conditions due to factors like the environment, climate, and fish types,resulting in inaccurate equipment selection outcomes. Recognizing this limitation, this paper proposes to apply CFD simulation of the temperature field to accurately calculate the heat exchange value between indoor air and water, thereby predicting the heat exchange values during aquaculture activities in the aquaculture workshop. providing a new approach for equipment selection. This paper selects a puffer fish breeding workshop in Dalian as the simulation object, establishing a 3D unsteady-state Computational Fluid Dynamics model. The model considers outdoor temperature, solar radiation, and phase-change heat transfer in water. Comparison with experimental data reveals a root mean square error of 0.46°C for the simulated results. During summer, the highest cooling load occurs at 16:00, reaching 94.6 kW. It is recommended to employ the Daikin GCHP-40MAH ground source heat pump as the water temperature control equipment. CFD simulation validates its effectiveness in shaping the indoor temperature field post-installation. the investment in water temperature control equipment can be reduced to a certain degree. This provides a reference value for the selection of water temperature equipment in aquaculture workshops.

Summer and winter performance evaluation of double layered PTFE dome with aerogel-glass wool thermal insulation- field experiments and numerical simulation

Achieving optimal thermal performance in large membrane-enclosed buildings presents a significant challenge. Aerogel mats offer potential for enhanced insulation, but their effectiveness in varying seasonal conditions is yet to be fully determined. The current study investigates the thermal performance of an enclosed dome with double-layered PTFE membrane roof integrated with aerogel-glass wool insulation mats. This research utilizes field experiments to assess indoor thermal comfort and numerical model to evaluate the effectiveness of the dome’s insulation capabilities during summer and winter conditions. The Aerogel integrated areas show a difference of 10.26/3.72 °C for summer/winter while that of 9.36/3.22 °C for glass wool-integrated areas. An online thermal comfort tool utilized maximum indoor temperature values during both seasons obtained from numerical results indicating that winter comfort falls within ASHRAE comfort range with PMV value −0.48 but summer conditions require enhancements. Additionally, the study assessed the insulation’s effectiveness using Maximum Reduction in Temperature (MRT), Decay Factor (DF), and Time Delay (TD) indicators. Results showed significant thermal lag, with peak temperature delays of up to 3.1 h in summer and 2.2 h in winter. Notable temperature differences between the dome’s inner and outer surfaces, alongside 28 % and 27 % DF reductions during summer and winter, respectively, demonstrated the roof insulation’s effectiveness. These findings support the use of both experimental and numerical methods to develop thermal management practices that improve sustainable building design, emphasizing occupant comfort and energy efficiency.

Enhancing Thermal and Daylight Performance of Historic Buildings with Passive Modifications; A Tropical Case Study

Historical buildings worldwide hold immense potential for adaptive reuse within the framework of the circular economy, a concept gaining traction in the building construction industry for its role in reducing carbon emissions. Many of these structures boast passive building design strategies, which inherently lower energy demands. Among these, thermal mass and daylight harvesting emerge as pivotal strategies for achieving indoor thermal and visual comfort, respectively. However, with the advent of significant climate change and evolving built environments, the efficacy of these historical passive strategies is subject to debate. This study focuses on a residential building constructed in the 17th century by Dutch settlers in Galle, Sri Lanka, serving as a case study. The research targets improvements in both thermal and visual comfort. Thermal performance analysis was conducted through air temperature measurements, revealing a notable 3-hour time lag and a 2.5°C reduction in peak air temperature during the day. Conversely, nighttime measurements indicated a rise in indoor temperature compared to ambient conditions. Using Design Builder software, the building was modelled to assess its daylighting conditions. Drawing upon thermodynamic principles and daylight harvesting techniques, the study proposes building envelope interventions and ventilation strategies to address nighttime overheating and enhance daylight utilization. The results demonstrate that these modifications can potentially reduce nighttime heat by 2-3°C, while also decreasing the energy requirement for lighting comfort from 51.10 kWh/m² per annum to 44.84 kWh/m² per annum. This research showcases the effectiveness of judiciously implemented interventions in historical buildings, illustrating tangible improvements in both thermal/visual performance and energy efficiency. Leveraging inherent qualities of historical structures and integrating modern design strategies to these buildings can play a vital role in sustainable urban development and energy conservation efforts.

Energy efficiency evaluation of green roofs as a passive strategy in the mediterranean climate

Green roofs are comprised of living vegetation that is installed on rooftops, serving as a sustainable architectural tool that offers numerous environmental, economic, and social advantages to the community. Green roofs have emerged as a prominent sustainable development strategy and architectural element in numerous urban centres worldwide. As for the effectiveness of green roofs in terms of boosting energy conservation in the Mediterranean climate of Amman, this research focuses on a standard medium-sized office building in Jordan. The main evaluation is carried out on the feasibility of enhancing the passive sustainability and energy performance of the building by applying extensive green roof technology. To determine the energy-saving potential, an annual energy demand in the absence of a green roof is estimated by a simulation program. In this paper, the analyses were performed in the form of parametric studies for assessing the impact of variation in soil depths on energy use, indoor temperature and carbon reduction. The study also indicates that green roofs can store heat and provide insulation enough to lessen the demand for comfortable temperature regulation indoors. The findings on energy savings indicate that energy consumption had reduced annually by up to 12 %. This means that large-scale green roofs have the real opportunity to become an efficient sustainable passive design strategy for increasing the energy performance of office building in Amman climatic conditions.

When thermochromic material meets shape memory alloy: A new smart window integrating thermal storage, temperature regulation, and ventilation

Traditional windows have poor thermal insulation performance, resulting in significant indoor heat loss in winter and outdoor heat entry in summer. Thermochromic smart windows can effectively block solar radiant heat by automatically adjusting light transmittance, thereby reducing air conditioning loads and leading to significant energy savings. In this study, the poly N-isopropyl acrylamide (PNIPAm)-based thermochromic hydrogel, modified MXene nanoparticles, and NiTi shape memory alloy (SMA) are integrated to endow the smart window with heat storage, temperature control, and ventilation. The smart window achieves 88.6% visible light transmission and 70% solar modulation. The inclusion of MXene nanoparticles further enhances photothermal response efficiency, while the ventilation system ensures efficient and fresh indoor air circulation. Compared to the common glass, the smart window reduces the indoor temperature by 8 °C, demonstrating its excellent temperature regulation ability. Simulation results indicate that in Shanghai, Cairo, Singapore, and Kuwait, the employment of thermochromic smart windows can reduce heating, ventilation, and air conditioning energy consumption (HVAC) by 32.6%, 49.9%, 42.7%, and 34.1%, respectively. This versatile thermochromic smart window is expected to significantly improve building efficiency and occupant comfort, offering a sustainable solution for future building designs.

Comparison of different control methods on the thermally activated building system (TABS) with large energy flexibility

Thermally Activated Building Systems (TABS) has great energy flexibility potential for the effective demand response, but the inherent large thermal inertia creates challenges for the control. Model Predictive Control (MPC) can improve the control efficiency while achieving certain objectives. However, the modeling mechanism of the TABS thermal behavior can lead to the difference in the controller performances. In this study, the white-box model, grey-box model, and black-box model based MPCs (referred to as W-MPC, G-MPC, and B-MPC) are developed and analyzed in depth for a case TABS with high heat flexibility potential. The performances of MPC strategies are investigated together with the baseline Rule Based Control (RBC) strategy under normal conditions as well as a variety of uncertainty events. In general, MPC strategies show better performance compared to the RBC. The prediction-based control can effectively maintain the indoor temperature within the constraints considering the uncertain disturbances and the gradual outdoor climate change. Moreover, MPC strategies help to increase the cost savings due to the effective load shifting by 30%–50%, and enhance the flexible energy utilization by 14%–29%. In terms of the comparison among the different MPC strategies, W-MPC shows better performance in the room temperature control, which reduces the violation of the indoor temperature constraint by 30% and 15% compared to G-MPC and B-MPC, respectively. G-MPC shows slight superiority concerning the economy and flexible energy usage. The normalized cost saving of G-MPC is approximately 3% and 8% lower compared to the W-MPC and B-MPC, respectively, while the utilization efficiencies of the flexible energy are approximately 6% and 12% higher.

Performance analysis of ground source heat pump systems for constant temperature and humidity air conditioning in industrial buildings: a case study

Abstract The ground source heat pump (GSHP) system has gained widespread popularity for its provision of efficient and environmentally sustainable cooling and heating solutions for buildings. While the performance of GSHP systems has raised significant concerns, research predominantly focuses on residential and office buildings, leaving a gap in understanding their applicability in industrial settings with constant temperature and humidity air conditioning needs. This study aims to address this gap by evaluating the performance of a hybrid ground source heat pump (HGSHP) system implemented within a cigarette factory located in the hot summer and cold winter (HSCW) of China. Through a comprehensive analysis of operational data and long-term monitoring under three cooling conditions, essential parameters including water temperature characteristics, indoor temperature and humidity, system efficiency, power consumption, and soil temperature distribution were examined. The findings revealed that the weighted average coefficient of performance (COP) of the heat pumps (COPhp) and the whole system (COPsys) was decreased from 5.05 to 4.32 and 3.32 to 2.89, respectively. A positive correlation was observed between low cooling load high energy consumption, and low COP. The inlet temperature of the condenser exhibited a declining trend, which was attributed to the intermittent operation of condensation heat recovery. Furthermore, a comprehensive analysis of indoor temperature and humidity distribution within the controlled room was presented and found that almost all the rooms serviced by this system meet the specified design requirements. This research contributes to an enhanced understanding of the viability of HGSHP systems in industrial settings and provides valuable references for improving their energy efficiency and overall performance.

Development of an AI Model Utilizing Buildings’ Thermal Mass to Optimize Heating Energy and Indoor Temperature in a Historical Building Located in a Cold Climate

Historical buildings account for a significant portion of the energy use of today’s building stock, and there are usually limited energy saving measures that can be applied due to antiquarian and esthetic restrictions. The purpose of this case study is to evaluate the use of the building structure of a historical stone building as a heating battery, i.e., to periodically store thermal energy in the building’s structures without physically changing them. The stored heat is later utilized at times of, e.g., high heat demand, to reduce peaking as well as overall heat supply. With the help of Artificial Intelligence and Convolutional Neural Network Deep Learning Modelling, heat supply to the building is controlled by weather forecasting and a binary calendarization of occupancy for the optimization of energy use and power demand under sustained comfortable indoor temperatures. The study performed indicates substantial savings in total (by approximately 30%) and in peaking energy (by approximately 20% based on daily peak powers) in the studied building and suggests that the method can be applied to other, similar cases.

Reversible thermochromic K2O·nSiO2-based anti-fire glass with bidirectional responsion and memory function

We describe a low-cost method for the synthesis of reversible thermochromic K2O·nSiO2-based anti-fire materials with bidirectional responsion and memory function, which was significant for energy-saving and fire-resistant applications. Here, the reversible thermochromic (RTC) and inverted reversible thermochromic (IRTC) K2O·nSiO2-based anti-fire glass were prepared via an in-situ reaction using a high solid content and low viscosity SiO2 sol (HSLV SiO2 sol, 55 wt%). The morphological, thermochromic, thermal insulation, and memory performances were systematically characterized. The pincer-like structure, rod-like structure and the layered microporous morphology were clearly shown in scanning electron microscopy (SEM) micrographs. The color change, foaming, and memory mechanisms were further investigated in detail. The significant differences appeared in the visible region of RTC and IRTC K2O·nSiO2-based anti-fire glass during heating and cooling process. FTIR spectra were used to characterize the difference among RTC, IRTC and control samples. The results indicated that a small amount of NH4+ caused the K2O·nSiO2-based anti-fire glass to exhibit IRTC characteristics, and the continuous addition of polyhydric alcohols was the key to obtain RTC performance. The IRTC K2O·nSiO2-based anti-fire glass was suitable for the conservatories or greenhouses, and the RTC K2O·nSiO2-based anti-fire glass reduced the direct sunlight exposure to control the indoor temperature. Combined with morphological and Tg analysis, the thermal insulation performance was enhanced by a microporous insulating layer with a sponge-like structure. We also used the bidirectional reversible thermochromic performance to prepare a novel anti-fire glass component for construction with anti-counterfeiting function. This work provided a new approach for the application of multifunctional smart windows.

The effect of indoor temperature on work performance of fifty-eight people in a simulated office environment

There is general agreement that indoor thermal conditions affect Office Work Performance (OWP); however, consensus on the specific characteristics of this relationship, such as its shape, effect size, and prediction accuracy, remains elusive. To address this gap, we conducted a study with fifty-eight participants exposed to temperatures (23°C, 25°C, 27°C, and 29°C) in a simulated tropical office environment over 165min to determine how temperature affects OWP. These temperatures were chosen to mirror typical conditions in tropical buildings. We maintained constant variables, including relative humidity (50%), clothing (0.5clo), metabolic rate (1.1MET), and airspeed (0.1ms-1). We measured OWP using one cognitive ability and seven neuropsychological tests targeting executive functions like flexible thinking, working memory, and self-control. OWP remained constant across all tested temperatures, despite some participants reporting thermal discomfort at the higher temperatures. Our findings support the Maximum Adaptability Model – aka extended-U model – which claims that human performance remains relatively stable over a wide range of thermal conditions typically found indoors. Given the consistent performance within the typical temperature range for tropical office buildings and participants’ preferred and accepted 25°C the most, we caution against lowering air conditioning set points below this level as it may lead to unnecessary cooling. We shared the database and code, to ensure our research data is openly available and our methodology can be used in other climates to reproduce our results.

Surrogate-Assisted and Filter-Based Multiobjective Evolutionary Feature Selection for Deep Learning.

Feature selection (FS) for deep learning prediction models is a difficult topic for researchers to tackle. Most of the approaches proposed in the literature consist of embedded methods through the use of hidden layers added to the neural network architecture that modify the weights of the units associated with each input attribute so that the worst attributes have less weight in the learning process. Other approaches used for deep learning are filter methods, which are independent of the learning algorithm, which can limit the precision of the prediction model. Wrapper methods are impractical with deep learning due to their high computational cost. In this article, we propose new attribute subset evaluation FS methods for deep learning of the wrapper, filter and wrapper-filter hybrid types, where multiobjective and many-objective evolutionary algorithms are used as search strategies. A novel surrogate-assisted approach is used to reduce the high computational cost of the wrapper-type objective function, while the filter-type objective functions are based on correlation and an adaptation of the reliefF algorithm. The proposed techniques have been applied in a time series forecasting problem of air quality in the Spanish south-east and an indoor temperature forecasting problem in a domotic house, with promising results compared to other FS techniques used in the literature.

Experimental Study on the Performance of an Air Conditioning Unit with a Baffled Indirect Evaporative Cooler

Indirect evaporative coolers (IECs) use the latent heat of water evaporation to cool air. This system has the advantage of operating at low power without a compressor and does not increase the absolute humidity of the air. However, an IEC is difficult to use on its own because its cooling capacity is limited by the theoretical constraint of the wet-bulb temperature of the ambient air. Therefore, an air conditioning unit (ACU) was integrated with an IEC and experimentally evaluated in this study. The dry and wet channels of the IEC were integrated with an ACU evaporator and a condenser, unlike previous studies where IECs were integrated solely with either an evaporator or a condenser. This reduced the cooling load on the evaporator and helped the condenser to dissipate heat to improve the performance of the existing ACU. In addition, the IEC was equipped with baffles to improve its performance. To assess the extent of the performance improvement due to integration with the IEC, comparisons were also performed under the same experimental conditions with an ACU only. The results showed that under conditions with an indoor temperature of 32 °C, integrating the IEC with the ACU increased the average cooling capacity by 13.1% and decreased the average power consumption by 8.60% during the test period, compared to using only the ACU. Consequently, the average coefficient of performance (COP) increased by 19.5% compared to using only the ACU under the same conditions.

Evaluating Thermal Comfort and Overheating Risks in A Social Housing Prototype: As Built Versus Retrofit Scenarios

Climate change has highlighted the importance of thermal comfort and its health-related outcomes, particularly for the most vulnerable members of society living in social housing. Due to their vulnerable living conditions, low-income people are more exposed to negative outcomes of overheating and cold indoor temperatures in buildings. Previous studies suggest that there is a significant risk of overheating in retrofitted buildings both for the current and future weather scenarios. The UK government has introduced new building regulations to assess and limit the risk of overheating in new buildings; however, there is still a need to assess and improve conditions for existing and retrofitted properties. This study aims to evaluate the effect of retrofit strategies on thermal comfort and the risk of overheating in social housing under current and future climatic conditions. A typical case study building was simulated in DesignBuilder to assess thermal comfort conditions for upgraded building fabric to Part L of the UK building regulations and Passive House standards. The summer results were analyzed according to CIBSE TM59 while the Predicted Mean Vote index (PMV) was used for winter analysis. Findings revealed that the south-facing bedrooms are most exposed to overheating. Risk of overheating significantly increased for the future weather scenarios by up to 10 times while winter thermal comfort improved for the retrofitted scenarios.

Energy management of greenhouses in micro grids: A case study

The nexus of population growth, constrained access to agricultural land, and enhanced living standards has necessitated the expansion of commercial greenhouses. These controlled environments are vital for shielding produce from unpredictable weather patterns and ensuring the availability of high-quality fresh produce throughout the year. Greenhouses maintain precise control over key parameters such as indoor temperature, humidity, CO2 concentration, and lighting, which are pivotal for optimal plant growth and product quality. However, the stringent control of these variables often results in substantial energy consumption, posing significant environmental and economic challenges. This research explores optimizing the internal environment of greenhouses while curbing energy usage, thereby mitigating potential environmental impacts. By focusing on grid-connected modes of operation, this study addresses the unique challenges posed by limited access to local energy resources. It explores the integration of microgrids, renewable energy sources, and combined heat and power systems to maintain operational efficacy and sustainability.

EXPLORING THE POTENTIAL OF VEGETATION STRUCTURES (VS) AND LANDSCAPE ORIENTATION IN MINIMIZING THERMAL PERFORMANCE IN A TROPICAL CLIMATE: A FIELDWORK STUDY

The study investigates the impact of vegetation structure within two landscape orientations on thermal performance, a critical consideration in urban planning and environmental management. Given the increasing concerns about Urban Heat Islands and global warming, understanding how vegetation influences thermal regulation is essential. This research employed a quantitative method through field measurement studies, focusing on two single-storey semi-detached houses namely East Landscape Orientation (ELO) and West Landscape Orientation (WLO) located at Seri Iskandar, Perak Malaysia. The snowball technique was utilized to select the case study houses, ensuring a targeted approach to understanding the effects. Thermal Microclimate Data Logger and Indoor Air Quality Monitor Data Logger were used to measure environmental parameters for thermal conditions, ensuring consistent, reliable, and comprehensive data for analyzing thermal performance in both outdoor and indoor environments. The research findings reveal that the orientation of a house relative to the sun's path significantly impacts its thermal dynamics, with vegetation playing a crucial role in moderating these effects. ELO houses benefit from milder morning sunlight and effective afternoon cooling, while WLO houses experience higher outdoor and indoor temperatures due to intense sunlight and less efficient transpiration by vegetation. Vegetation on the west side provides shade and reduces heat absorption, improving comfort and energy efficiency. Understanding these dynamics can help homeowners and architects make informed decisions about landscape design, enhancing thermal comfort and energy efficiency in homes. This research also contributes to the expanding knowledge base on sustainable urban design by exploring the use of vegetation to enhance thermal performance.