Search Results (208)

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 addresses the critical issues of rigid energy use and insufficient demand-side responsiveness in office buildings’ Variable Refrigerant Flow (VRF) systems under complex summer conditions. Existing research lacks fine-grained characterisation of short-term load fluctuations and often fails to accurately couple energy efficiency with humidity-adapted thermal comfort. To fill this gap, this paper proposes an integrated Model Predictive Control (MPC) framework driven by load characteristic identification and a novel hybrid prediction model. First, based on actual hourly metered data (683,280 records), K-means clustering was employed to identify three typical load patterns, pinpointing short-term peak loads in core office zones as the primary target for flexible regulation. Second, a high-precision GS-DBO-ELM prediction model—integrating Grid Search and Dung Beetle Optimisation—was developed to capture the nonlinear dynamics of VRF energy consumption and Predicted Mean Vote (PMV). The model achieved an R² of 0.99 with relative errors constrained within ±5%. Finally, a multi-objective MPC strategy, solved via an improved Artificial Hummingbird Algorithm (HAGSAHA) and weighted by the Analytic Hierarchy Process (AHP), was implemented to dynamically adjust zone-level temperature setpoints. Results demonstrate that the proposed MPC strategy reduces daily cooling energy consumption by 7.95–10.69% and peak loads by 15.3%, while maintaining strict thermal comfort (PMV within ±0.5). Under a time-of-use pricing mechanism, the flexible scheduling strategy achieved a 12.37% total electricity reduction and a 9.54% reduction in operating costs. This work provides a highly replicable, climate-tailored solution for low-carbon, flexible energy management in public buildings. Full article

To improve the indoor thermal comfort of embedded atriums and corridors in office buildings during summer, this study aims to optimize air conditioning airflow organization in atriums using computational fluid dynamics (CFD) simulations. Field measurements were carried out to collect air parameters, which were subsequently used to validate the established CFD model. Taking a six-story office building in Xi’an as the research subject and stratified air conditioning as the baseline case, this study investigated the effects of air inlet layout, air inlet type, and air volume distribution on the indoor thermal environment. The results revealed significant vertical temperature stratification within the atrium, with average temperatures ranging from 23.5 °C to 46.1 °C. Based on comparative analysis of multiple optimization scenarios, the following conclusions are drawn: adopting swirl diffusers in the corridors with an air inlet quantity ratio of 1:1:1:1:2 from the first to fifth floors, combined with uniform air supply volume across the first to fourth floors, can maintain the average Predicted Mean Vote (PMV) of each floor within the range of −0.1 to 0.3. Conversely, excessive air supply volume on upper floors and insufficient air supply volume on lower floors significantly degrade the corridor thermal comfort. Full article

The WELL Building Standard (WELL) is a certification system designed to enhance occupant health and well-being in indoor environments. Conventional building energy-saving strategies typically rely on fixed temperature setpoint adjustments, which may conflict with WELL thermal comfort requirements. However, achieving high energy efficiency remains essential. This study uses a quantitative evaluation framework with TRNSYSs to examine the effectiveness of integrating load reset control (LRC) into simultaneous heating and cooling (SHC) systems. It compares LRC with conventional fixed setpoint (SP) and predicted mean vote (PMV) control strategies, based on WELL’s thermal comfort criteria (maintaining the PMV between −0.5 and +0.5). Six simulation cases were analyzed, considering radiant (RAD) and convection (CONV) terminals. The results indicate that radiant terminals provide more stable PMV performance while consuming less energy than convection terminals, demonstrating better compliance with WELL objectives. Although PMV control achieves the highest thermal comfort, it substantially increases energy consumption. In contrast, LRC emerges as an optimal strategy, effectively balancing the energy efficiency of SP control with the comfort of PMV control. The RAD-LRC configuration delivers the best overall performance. It achieves higher thermal comfort than SP, with comparable energy consumption, making it a highly practical approach for modern building energy management. 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

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

To address the critical need for accurate human thermal comfort prediction in winter heating environments, this study established a comprehensive thermal comfort dataset containing 2089 valid samples through experiments. On this basis, thermal comfort prediction models were constructed using three multi-class machine learning algorithms: Support Vector Classification, K-Nearest Neighbors, and Random Forest. The predictive performance of 63 different feature combinations was systematically evaluated. The results indicate that the feature subset comprising indoor air temperature, forehead temperature, cheek temperature, dorsal hand temperature, heart rate, and systolic blood pressure yields the optimal prediction performance. Among the evaluated models, the Random Forest model demonstrated superior overall performance, achieving an accuracy exceeding 90% and an AUC ranging from 96% to 99%, significantly outperforming the SVC and KNN models. Compared with the traditional Predicted Mean Vote (PMV) model, the machine learning models developed in this study showed a substantial improvement in prediction accuracy under identical conditions; notably, the Random Forest model improved accuracy by approximately 40% over the PMV model. Based on these findings, a smart heating system framework integrating environmental sensors, wearable devices, and intelligent control valves is proposed, providing a theoretical basis and technical approach for realizing personalized and energy-efficient heating control. 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

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

The three key design criteria for nearly zero-energy buildings (nZEBs) and climate-neutral buildings are minimizing energy use, ensuring high occupant comfort, and reducing environmental impact. Thermal comfort is one of the main components of indoor environmental quality (IEQ), strongly affecting occupants’ health, well-being, and productivity. As energy-efficiency requirements become more demanding, the appropriate selection of heating systems, their automated control, and the management of solar heat gains are becoming increasingly important. This study investigates the influence of two low-temperature radiant heating systems—underfloor and wall-mounted—and the use of Venetian blinds on perceived thermal comfort in a highly glazed public nZEB building located in a densely built urban area within a temperate climate zone. The assessment was based on the PMV (Predicted Mean Vote) index, commonly used in IEQ research. The results show that both heating systems maintained indoor conditions corresponding to comfort or slight thermal stress under steady state operation. However, during periods of strong solar exposure in the room without blinds, PMV values exceeded 2.0, indicating substantial heat stress. In contrast, external Venetian blinds significantly stabilized the indoor microclimate—reducing PMV peaks by an average of 50.2% and lowering the number of discomfort hours by 94.9%—demonstrating the crucial role of solar protection in highly glazed spaces. No significant whole-body PMV differences were found between underfloor and wall heating. Overall, the findings provide practical insights into the control of thermal conditions in radiant-heated spaces and highlight the importance of solar shading in mitigating heat stress. These results may support the optimization of HVAC design, control, and operation in both residential and non-residential nZEB buildings, contributing to improved occupant comfort and enhanced energy efficiency. Full article

This paper is dedicated to investigating how short-term indoor temperature drift influences occupants’ thermal sensation in residential nZEB buildings and how this affects the applicability of steady-state comfort prediction. Residential buildings frequently operate under transient conditions, where the classical PMV approach may deviate from reported sensation. The objective of this paper is to evaluate the agreement between steady-state PMV and occupants’ thermal sensation votes under winter conditions to test a regression-based correction index A_eff and an adjusted indicator PMV_adj while preserving the PMV concept. The study uses high-resolution measurements of indoor air temperature and mean radiant temperature synchronised with TSV responses, followed by statistical evaluation using error metrics and correlation analysis. The results show that baseline PMV correlates well with TSV but exhibits a consistent magnitude mismatch under transient conditions. The proposed PMV_adj reduces this mismatch, decreasing NRMSE from 17.61% to 14.00% and slightly improving agreement with Pearson r = 82.18%, R² = 67.54%. Regression analysis shows that A_eff is strongly associated with the indoor air temperature drift rate ΔT_in/Δt with R² = 0.6805, but has a weaker relationship with ΔT_MRT/Δt, R² = 0.1851. The research provides a practical basis for improving PMV-based comfort assessment during winter operation in residential nZEB. Full article

The concept of carbon-neutral buildings encompasses not only carbon emission reductions but also sustainability. Building sustainability includes the physical durability of the structure, the health and safety of its tenants, and harmony with the surrounding environment. The achievement of these goals requires alignment among diverse stakeholders associated with buildings; however, such alignment is limited by economic (cost), environmental (global warming), and social (institutions and policies) factors. This study proposes an operation model that integrates buildings, the carbon permit market, and deep reinforcement learning (DRL) to address these limitations. The DRL model reduces energy consumption while maintaining indoor comfort, generates carbon permits equivalent to the amount of energy saved, and creates a new revenue stream by selling them. To achieve more precise comfort management, the model incorporates a policy that combines predicted mean vote (PMV) and Humidex. In the context of a privately owned commercial office building, the DRL model achieved indoor comfort levels of 98.51% for PMV and 97.22% for Humidex, while reducing energy consumption by 34,376 kWh, lowering carbon emissions by 26,607 kgCO₂eq, and generating USD 176 in carbon permit revenue. These results translated into a total reduction in operating costs of 7.5%, amounting to USD 2951. Consequently, the proposed approach provides cost reductions for building owners, comfort for tenants, efficiency for managers, and carbon emission reductions that contribute to carbon neutrality. Full article