Search Results (77)

This study presents a coupled building simulation framework that evaluates thermal and daylight performance concurrently within a unified multi-objective decision space. Unlike conventional sequential workflows, where daylight metrics are assessed after energy optimization or used primarily for compliance verification, the proposed approach embeds EnergyPlus and Radiance simulations directly within the same optimization loop. This structure enables a systematic exploration of non-linear interactions between Energy Use Intensity (EUI), cooling loads, Spatial Daylight Autonomy (SDA), and Annual Sunlight Exposure (ASE) during early-stage façade design. The framework is demonstrated through a medium-rise office building in India’s National Capital Region, a composite climate characterized by strong seasonal and directional variability. Parametric variation in façade orientation, window-to-wall ratio, and external shading configurations was explored using a multi-objective genetic algorithm to identify Pareto-optimal performance regimes. The results reveal distinct orientation-dependent trade-off structures between solar exposure, cooling demand, and daylight availability that are not evident in rule-based or sequential simulation approaches. In particular, a transitional East-facing façade regime emerges in which balanced shading and glazing proportions achieve near–North-facing cooling performance while maintaining high daylight autonomy under controlled sunlight exposure. Rather than proposing a single optimal solution, the study demonstrates how tightly coupled thermal–daylight simulation can function as a knowledge-discovery tool, enabling the extraction of transferable façade response patterns from simulation outputs. The findings highlight the limitations of prescriptive orientation hierarchies in composite climates and illustrate the value of integrated simulation workflows for performance-driven early-stage design across diverse climatic contexts. Although the study references thermal performance, the optimization objectives are limited to peak cooling load and annual Energy Use Intensity (EUI). Occupant comfort indices such as Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) were not explicitly simulated. Therefore, results are interpreted as energy–daylight performance optimization rather than direct thermal comfort optimization. Full article

This study investigates the effects of vehicle air-conditioning parameters on cabin thermal environment and occupant comfort. Computational fluid dynamics and discrete particle simulations involving different inlet-vent angles, inlet relative humidity (RH) levels, and occupant counts were conducted to analyze airflow, temperature, and RH. Thermal comfort was assessed using predicted mean vote (PMV), predicted percentage of dissatisfied (PPD), equivalent homogeneous temperature, and mean age of air (MAA). As a result, the uniform airflow at a 30° inlet angle provided the best global thermal comfort based on PMV (0.49) and PPD (10.02), whereas a 0° inlet angle improved local comfort around the chest area. Maintaining an inlet RH of 40–50% enhanced overall thermal comfort. Increasing the occupant counts raised the average cabin temperature to 301.76 K (Case 9), while also affecting local airflow patterns and MAA distributions; the addition of rear-seat occupants increased the local temperature around the driver’s left hand. These findings provide practical guidance for vehicle heating, ventilation, and air-conditioning system design, indicating that ventilation strategies should consider global comfort indices, localized airflow, thermal patterns, and particle removal performance. Overall, this parametric study highlights the association between vehicle cabin conditions and thermal comfort, providing baseline data for digital twin–based adaptive ventilation systems. Full article

This study examines the gap concerning occupants’ perceived thermal comfort and objectively measured indoor conditions in a green university building in Riyadh. The purpose is to assess occupant satisfaction with thermal conditions, compare subjective responses with physical measurements, and derive design and operational implications for educational buildings in hot-arid climates. The primary aim was to assess occupant satisfaction with indoor thermal conditions and to measure key environmental parameters to provide a thorough assessment of thermal comfort. A cross-sectional approach was used, combining subjective data from the Center for the Built Environment (CBE) Occupant Indoor Environmental Quality (IEQ) survey with objective measurements of air temperature, relative humidity, mean radiant temperature, and air velocity, which were documented over five consecutive working days during the mid-winter period in Riyadh. These parameters were explored using the CBE Thermal Comfort Tool to calculate Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) indices. Statistical analyses examined the relationship between occupant-reported comfort and measured environmental conditions. Results showed that only 36% of occupants reported satisfaction with thermal comfort, while 48% expressed dissatisfaction. In contrast, objective measurements indicated stable indoor conditions within recommended comfort ranges (average temperature 23 °C, humidity 30–34%, MRT 24 °C, air velocity 0.5–1.0 m/s), with PMV values near neutral (−0.2 to 0.0) and PPD below 6%. The observed discrepancy highlights the influence of regional climate, individual adaptability, and perceived control. These findings emphasize the need to integrate both subjective feedback and objective measurements to develop occupant-centered strategies that enhance comfort and well-being in sustainable educational buildings in hot-arid climates. Full article

Winter thermal comfort in university classrooms in China’s Hot Summer and Cold Winter (HSCW) regions remains problematic due to mismatches between institutional heating setpoints and students’ actual thermal preferences. To investigate students’ thermal perceptions and behavioral responses, a post-occupancy evaluation (POE) survey was conducted, followed by field measurements in a typical classroom in Chengdu under three conditions: no-heating condition, heating conditions at 20 °C and 25 °C. Indoor environmental parameters were continuously monitored, and thermal comfort was assessed using the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) model. The results show that no-heating conditions were unacceptable, highlighting the necessity of heating. While the 20 °C setpoint provided partial improvement, thermal comfort was not consistently achieved throughout the day. In contrast, the 25 °C setpoint maintained near-neutral conditions during most occupied periods. In addition, a pre-heating duration of approximately 30 min was found to be essential for reducing initial thermal discomfort. Overall, the findings indicate that fixed institutional heating standards may not adequately satisfy students’ thermal needs. Adaptive heating strategies that combine appropriate setpoints with sufficient pre-heating duration are therefore recommended to balance thermal comfort and energy efficiency in university classrooms in the HSCW regions. Full article

Escalating climate change and the increasing frequency of weather extremes pose a threat to the resilience of urban environments and human health, highlighting the urgent need for implementing energy-efficient interventions and reducing building cooling loads. This study investigates the passive building envelope retrofit technologies of external shading, electrochromic windows, and thermochromic windows through a multi-criteria evaluation analysis based on energy savings, economic performance, and indoor thermal comfort improvement. Thermochromic windows are discerned by a mean colour transition temperature of 34 °C and operate throughout the entire year, while electrochromic windows are activated only during cooling periods. Both technologies present total solar transmittance indices of 72.6% and 8.4% in the bleached and tinted state, respectively. External shading devices are either static or movable, applied with an inclination angle, and are either standalone interventions or combined with chromogenic glazing. Eight retrofit scenarios are investigated for a single-story, fully electrified residential building in Athens, Greece. The building features south- and east-oriented windows, which is an appropriate case to assess the effectiveness of these passive envelope cooling technologies in regulating solar heat gains. Thermal comfort is assessed using Fanger’s PMV (predicted mean vote) and PPD (Predicted Percentage of Dissatisfied) indices. The combination of electrochromic windows and movable external shading yields the highest annual electricity savings at 22.2% and reduces the PPD by 15.8%. Local static shading, on the other hand, ranks as the optimal retrofit solution in terms of economic performance, with a life-cycle cost of €6378, a 9.3% improvement in thermal comfort, and a corresponding reduction of 626 thermal discomfort hours. While the proposed multi-criteria framework can be applied to other buildings and climates, the quantitative results reported here are linked to the specific case examined: a residential building with south- and east-facing glazing in Athens, Greece, representing Mediterranean climatic conditions. Full article

Achieving Net-Zero Energy Building (NZEB) performance through retrofitting requires identifying optimal measures that effectively enhance energy efficiency. Determining these optimal retrofit strategies typically involves running thousands of building energy simulations, which imposes a substantial computational burden. To address this challenge, a novel machine learning-based framework is proposed to optimize retrofit strategies for NZEBs under future climate change scenarios. A Non-Dominated Sorting Genetic Algorithm (NSGA-III) is employed to minimize both annual energy consumption and the Predicted Percentage of Dissatisfied (PPD), while simultaneously ensuring net-zero energy balance, thereby generating a Pareto front of optimal solutions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank the Pareto-front solutions and identify the most favorable retrofit scenario. The results show that the proposed framework reduces optimization time by at least a factor of two compared with simulation-only optimization. Leveraging these computational savings, the framework evaluates a suite of passive and renewable measures across multiple future timeframes to capture the influence of climate change on retrofit performance. The findings indicate that achieving NZEB under future climate conditions requires higher levels of thermal insulation and greater renewable integration than under present-day conditions. Under the Shared Socioeconomic Pathways (SSP) framework, optimal insulation levels in the fossil fuel-dependent scenario are lower than in the sustainable scenario by up to 18% in C-type (warm temperate), 12% in D-type (snow), and 13% in E-type (polar) climates. The combined retrofit measures can reduce annual energy consumption by up to 80% and lower PPD by as much as 67% compared to the base case. Full article

Building energy renovation planning should be based on a multi-criteria evaluation that targets both reduced energy consumption and a high-quality indoor thermal environment. The present study investigates the building energy retrofit technologies of thermal insulation, highly insulative windows, mechanical ventilation for cooling purposes, and shading, aiming to identify the optimum energy retrofit strategy for different building typologies. Indoor thermal comfort is evaluated with the thermal comfort indexes of the predicted mean vote (PMV) and the Predicted Percentage of Dissatisfied (PPD). Each renovation scenario is evaluated in terms of thermal performance and thermal comfort, while an optimum retrofit scenario is defined as the one that simultaneously achieves the maximum decrease in the yearly energy demand and the greatest decrease in the building’s indoor thermal discomfort. The multi-objective analysis is performed using the EnergyPlus simulation engine, which is used to perform yearly dynamic simulations and provide accurate results. This study considers a typical one-story apartment building located in the city of Athens, Greece. According to the calculations, the retrofit strategy that combines all four examined interventions results in an 11.8% and 56.1% decrease in the building’s heating and cooling energy demand, respectively, while an annual enhancement of 16.6% in the building’s thermal comfort PPD index is calculated. Full article

Achieving adequate thermal comfort in classrooms in hot cities in southern Mexico is challenging. A heterogeneous distribution of air conditioning flow leads to thermal discomfort, affecting occupants’ academic performance and increasing energy consumption. This study evaluates the thermal comfort of occupants in an air conditioned classroom using computational fluid dynamics. We determined the effects of variations in air conditioning operating parameters (supply angle, velocity, and temperature) on PMV and modified PMV indices. An operating configuration of 60°, 3 m/s, and 22 °C ensures that thermal comfort remains within regulations while optimizing energy consumption, in contrast to the original PMV model. Using the modified PMV model, the values are 0.38 for students and 0.31 for the teacher, with percentages of dissatisfied individuals of 10% and 7.7%, respectively. This study demonstrates the importance of analyzing air conditioning operating parameters to enhance thermal comfort while reducing energy consumption. Full article

Sustainable renovation is a critical aspect for designing energy-efficient buildings with reasonable cost and high indoor living standards. The objective of this paper is to investigate various renovation scenarios for an old, uninsulated building with a floor area of 100 m² located in Athens, aiming to determine the global optimal solution through a multi-criteria analysis. The multi-criteria analysis considers energy, economic, and thermal comfort criteria to perform a multi-lateral approach. Specifically, the criteria are: (i) maximization of the energy savings, (ii) minimization of the life cycle cost (LCC), and (iii) minimization of the mean annual predicted percentage of dissatisfied (PPD). These criteria are combined within a multi-criteria evaluation procedure that employs a global objective function for determining a global optimum solution. The examined retrofitting actions are the addition of external insulation, the replacement of the existing windows with triple-glazed windows, the addition of shading in the openings in the summer, the application of cool roof dyes, the use of a mechanical ventilation system with a heat recovery unit, and the installation of a highly efficient heat pump system. The interventions were examined separately, and the combined renovation scenarios were studied by including them in the external insulation because of their high importance. The present study encompassed the investigation of a baseline scenario and 26 different renovation scenarios, conducted through dynamic simulation on an annual basis. The results of the present analysis indicated that the global optimal renovation scenario, including the addition of external insulation, the installation of highly efficient heat pumps, and the use of shading in the openings in the summer, saved energy by 74% compared to the baseline scenario. The LCC was approximately EUR 33,000, the simple payback period of the renovation process was around 6 years, the annual CO₂ emissions avoidance reached 4.6 tn_CO2, and the PPD was at 9.7%. An additional sensitivity analysis for determining the optimal choice under varying weights assigned to the criteria revealed that this renovation design is the most favorable option in most cases. These results prove that the suggested renovation scenario is a feasible and viable solution that leads to a sustainable design from multiple perspectives. Full article

Ensuring thermal comfort in waiting areas is essential for visitor satisfaction and well-being. In the context of nearly zero-energy buildings (nZEBs), these spaces—typically characterized by short-term occupancy, transient user behavior, and the need for rapid temperature adjustment—pose specific challenges for HVAC control in balancing comfort and energy demand. This study investigates the influence of a ceiling-mounted fan coil unit (FCU) operating in heating mode on thermal comfort conditions in an nZEB office waiting area. Measurements were conducted at multiple points within the space to assess microclimate parameters, followed by the calculation of the predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) indices, supported by occupant feedback collected through short interviews. The results showed that although the FCU effectively increased the average temperature, its intermittent operation and localized air jets during the heating phase caused temporary discomfort near the unit. Occupant feedback confirmed sensations of discomfort due to strong air movement during FCU operation but indicated slightly higher overall dissatisfaction and smaller variability compared to model-based PPD values, reflecting the averaging effect of occupant perception over time. These findings highlight the need for optimized FCU control strategies in waiting areas, such as operating at reduced fan speeds and preheating the heat exchanger, to enhance occupant comfort. This study contributes to improving HVAC control concepts for semi-transient spaces in nZEBs. Full article

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R² ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization. Full article

In this work, the indoor thermal environment and indoor air quality of rural houses in Northern China were investigated in detail. The current heating situation in rural areas, the causes of indoor air pollution, and the indoor ventilation habits of residents were analyzed. The indoor thermal environment and indoor air quality were improved by upgrading the thermal insulation of the rural housing envelope and installing indoor ventilation systems with heat recovery, leading to an average indoor temperature increase of 6 °C. The Predicted Mean Vote reached approximately 1.0, so the human body heat sensation was more moderate. The air age was greatly reduced, and the indoor air quality was significantly improved. The Predicted Percentage of Dissatisfied dramatically decreased to 15%. Thus, when focusing on heat source renovation in rural areas, priority should be given to improving the energy efficiency of buildings, especially the building envelope insulation performance. Ventilation and air exchange systems with heat recovery are inexpensive and effective, and they are suitable for rural dwellings where the temperatures are not as high as they should be but where the indoor air quality is poor and ventilation is urgently needed. Full article

Local data about indoor thermal comfort are in short supply, which are always different from the predicted results produced by models shown in previous studies. Shopping malls that consume substantial energy need to save energy, provided that thermal comfort is maintained. Therefore, this research investigated indoor thermal comfort using field measurements and questionnaires in a typical shopping mall in Danyang, China, with a hot summer and cold winter climate in order to explore local demands and energy-saving potential. The findings are as follows: (1) The average air temperature ($T_a$) and operative temperature ($T_{op}$) are 26.7 °C and 26.4 °C, which implies a minor influence from radiation and other factors on $T_a$. Women are more sensitive to changes in outdoor temperature since clothing insulation ($I_{cl}$) varies by gender: 0.31 clo and 0.36 clo for male and female individuals, respectively. (2) The thermal neutral temperature (TNT) derived from the thermal sensation vote (TSV) is 25.26 °C, which is significantly higher than the 21.77 °C obtained from the predicted mean vote (PMV) model. (3) There is a wide range of acceptable temperatures for thermal comfort because the highest temperature was identified by the thermal comfort vote (TCV) at 27.55 °C, followed closely by 27.48 °C, 26.78 °C, and 25.32 °C, which were separately derived from the thermal acceptance vote (TAV), TSV, and predicted percentage of dissatisfied (PPD) people; these were based on an upper limit of the acceptable 80% range. (4) In total, 94.85% of respondents accepted the indoor air quality, although the median concentration of CO₂ was 772 ppm, and the neutral relative humidity level was 70.60%. Meanwhile, there is an important relationship between air quality satisfaction and operative temperature; thus, the temperature (26.93 °C) with peak satisfaction can enhance air quality perception and thermal comfort. (5) The energy savings that can be achieved are 25.77% and 9.12% at most based on acceptable thermal comfort compared with baseline energy consumption at 23 °C and 26 °C, respectively. Full article

Researchers have increasingly focused on thermal comfort, examining both individuals’ thermal sensations and the percentage of people dissatisfied with the thermal environment. Most studies rely on the widely used PMV (Predicted Mean Vote) model and the PPD (Predicted Percentage of Dissatisfied) value derived from it, both defined by the ISO 7730:2005 standard. However, previous studies have shown that this standardized method only applies under steady-state conditions, which do not reflect the dynamic nature of everyday environments. As closed-loop control technologies gain prominence in building services, the need to evaluate thermal comfort under time-varying conditions has grown. The standard method does not account for the thermal inertia of the human body, which limits its applicability in such dynamic contexts. In this study, we develop a method to estimate instantaneous thermal sensation under non-stationary conditions by incorporating thermal inertia through signal processing techniques. This approach addresses a well-recognized limitation of the standard PMV–PPD model and provides a way to assess thermal comfort in real time. We collected experimental data using a thermal comfort measurement station, a thermal manikin, and human subjects in a controlled climate chamber. The proposed method enables real-time evaluation of thermal comfort in dynamic environments and offers a foundation for integration into HVAC control and comfort optimization systems. Full article

Buildings consume nearly 40% of global energy, necessitating innovative strategies to balance energy efficiency and occupant comfort. While shading and ventilation are critical to sustainable design, they are often studied independently, leaving gaps in understanding their combined potential. This study provides a novel quantitative analysis of dynamic shading and ventilation strategies, using a dataset of 5000 simulations in IDA Indoor Climate and Energy (IDA ICE) to reveal the synergies and trade-offs in building performance. Four distinct scenarios are analyzed: minimal shading and limited ventilation (shading factor $S_f$ = 0.0, ACH = 0.5), optimized shading and moderate ventilation ($S_f$ = 0.5, ACH = 1.5), dynamic shading and enhanced ventilation ($S_f$ dynamically adjusted, ACH = 2.5), and high shading with maximum ventilation ($S_f$ = 1.0, ACH = 3.0). The results show a progressive reduction in thermal discomfort, with the predicted percentage dissatisfied (PPD) decreasing from >80% in the first scenario to ~25% in the dynamic scenario and ~15% in the high shading scenario. The energy demand increases by up to 40% in the highest shading scenario, highlighting trade-offs. These findings underscore the importance of dynamically integrating shading and ventilation, providing actionable recommendations such as dynamic shading and night cooling that can reduce discomfort and improve energy efficiency by up to 30%. By bridging the research gaps, this study advances sustainable building design and offers a robust framework for creating energy-efficient, comfortable buildings. Full article