Indoor Environment Of Buildings Research Articles

Development of a back-propagation neural network and adaptive grey wolf optimizer algorithm for thermal comfort and energy consumption prediction and optimization

Heating ventilation and air conditioning (HVAC) systems provide a comfortable indoor thermal environment, but in the process of attaining appropriate indoor thermal comfort levels, they usually entail high energy consumptions. It is therefore imperative to balance thermal comfort value with energy consumption. However, such research currently faces two problems: one, it is difficult to obtain accurate parameters pertaining to the indoor environment of buildings, particularly near heat source areas; two, it is the diametrical nature of having to simultaneously maintain thermal comfort and keep energy consumption low. Therefore, this study aims to propose a rapid thermal comfort level prediction and optimization algorithm, as well as a method to minimize the energy consumption using only a computational fluid dynamic (CFD) database that is compact in size. Firstly, CFD is used to implement the database that stores data on indoor airflow and temperature distributions of different building structures and indoor conditions. Next, using the database as a basis, a back-propagation neural network (BPNN) is developed to predict the thermal comfort level. The adaptive grey wolf optimizer (GWO) algorithm is then applied to optimize the thermal comfort value, and the latest control methods: the artificial neural network (ANN)-genetic algorithm (GA) and ANN-particle swarm optimizer (PSO) algorithm are compared against the BPNN-based adaptive GWO method. The results show that the BPNN-based adaptive GWO algorithm can rapidly predict the thermal comfort level and have strong optimization ability. Meanwhile, 1.01% of energy savings are achieved.

A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings.

Monitoring the indoor environment of historic buildings helps to identify potential risks, provide guidelines for improving regular maintenance, and preserve cultural artifacts. However, most of the existing monitoring systems proposed for historic buildings are not for general digitization purposes that provide data for smart services employing, e.g., artificial intelligence with machine learning. In addition, considering that preserving historic buildings is a long-term process that demands preventive maintenance, a monitoring system requires stable and scalable storage and computing resources. In this paper, a digitalization framework is proposed for smart preservation of historic buildings. A sensing system following the architecture of this framework is implemented by integrating various advanced digitalization techniques, such as Internet of Things, Edge computing, and Cloud computing. The sensing system realizes remote data collection, enables viewing real-time and historical data, and provides the capability for performing real-time analysis to achieve preventive maintenance of historic buildings in future research. Field testing results show that the implemented sensing system has a 2% end-to-end loss rate for collecting data samples and the loss rate can be decreased to 0.3%. The low loss rate indicates that the proposed sensing system has high stability and meets the requirements for long-term monitoring of historic buildings.

Environmental quality in primary schools and related health effects in children. An overview of assessments conducted in the Northern Portugal

Since children are far more vulnerable than adults to the effects of air pollution and spend about a third of their day in school, strict control of indoor environments in educational buildings is paramount to properly identify, manage and mitigate putative environmental risks for children. In this context, this work aimed to provide a holistic view on the outcomes obtained from the national ARIA project, which was focused on broadening the knowledge on the effects on children’s health of the exposure to schools’ indoor air. In particular, this work performed a comprehensive investigation of the indoor air quality (IAQ) in 20 public primary schools located in Porto (Northern Region, Portugal). This paper presents the results of the investigation along with the recently published main outcomes of ARIA on (1) the characteristics of the environment surrounding the surveyed buildings, and (2) the school’s environment-related health effects in school-age children from the studied classrooms. The investigation consisted in an extensive assessment plan conducted in 71 classrooms to assess temperature, relative humidity, carbon monoxide and dioxide, particulate matter (PM2.5, PM10, and ultrafine particles), nitrogen dioxide, ozone, volatile organic compounds, formaldehyde, acetaldehyde, airborne fungi, bacteria and endotoxins, and investigate their respective sources, during the cold seasons of 2014–2015. A series of studies was also launched to investigate school environment-related health detriments in 916 children. The results showed that comfort and ventilation issues and/or indoor pollutant levels that exceeded recommended limits were found in a substantial number of the classrooms surveyed. The high density of occupants, deficient ventilation, soil characteristics, presence of indoor pollution sources (e.g. classroom materials and consumer products) and outdoor pollution were some of the factors that seemed to explain the high air stuffiness and/or indoor pollution load identified in classrooms. In fact, some of indoor pollutants detected, even at low exposure levels, were associated with the development of respiratory symptoms in school-age children. Moreover, results from this work have also shown that the characteristics of the environment surrounding the schools, namely the presence of green spaces and species richness, can be major determinants of respiratory health among school-age children. Overall, the body of evidence generated from the ARIA project can support new evidence-based perspectives for promotion of health in educational buildings.

The Spirit of Time—The Art of Self-Renovation to Improve Indoor Environment in Cultural Heritage Buildings

The purpose of this paper is to explore the challenges of an old low-standard urban district with a strong historical and cultural heritage and propose more sustainable renovation solutions, acceptable for the residents and municipality. The challenges of physical renovation or refurbishment are complex due to poor condition of the buildings, municipal ownership and governance, mixed management with community and low rents, which are insufficient to cover the costs. The paper discusses the proposed solutions of living standards, supported by the research in two directions: (i) available resources and reuse of materials, (ii) developing a renovation guidance for inhabitants from the building physics perspective, including indoor environment quality. Challenges related to energy efficiency are addressed from the decision-making perspective to overcome the barrier of lack of motivation to invest in energy-efficient measures at the individual and community level. The interdisciplinary approach complements engineering-focused studies with a focus on the comfort conditions and the influence of occupant habits in sustainable buildings. The methods used were literature review, case studies with observations and survey, looking to cover all technical, social, and historical aspects of sustainable renovation of cultural heritage buildings with the same level of importance. Results show that to keep a sustainable, low-cost urban living model, instructions for self-renovation are a valuable guidance for non-professional actors to make more sustainable choices. In conclusion, we can emphasize that inhabitants are accustomed to lower living standards, so the project is aimed to present the proper solutions for improvement as a balance between new sustainable technical solutions, personal self-renovation skills, habits, and health.

AN ASSESSMENT OF STUDENT'S COMFORT IN HIGHER EDUCATION BUILDING OF PAKISTAN

Comfort assessment, as defined in the literature, is an evaluation of the quality of indoor building environments through user perception. Considering its criticality in the educational institutes, this study was aimed to assess the students’ comfort level in the universities of Pakistan, so that proper measures can be carried out to upgrade the classroom’s environment. Mehran University of Engineering and Technology, Jamshoro, Pakistan was selected as the study area. Two departments, namely the Architecture Department and City and Regional Planning (CRP) Department were randomly selected. With an interval of ten, a sample of twenty-seven students was selected using Systematic Sampling Technique. The data was analyzed using frequency distribution and Likertscale index score method. Results showed strong satisfaction with the seating and acoustic quality, whereas dissatisfaction with the visual, ventilation and thermal qualities were observed. Classrooms of the Architecture Department with moderate room temperature had minimal access to sunlight and air. While the classrooms of the CRP Department had extreme temperature gain with excess sunlight and glare both during summer and winter season. Thus, it was proposed that the problematic conditions in these two Departments should be addressed at the earliest to improve the comfort level. Findings, also proposed the necessity to include such variables in the annual student's feedback.•Likewise, it was argued that a similar approach could be adopted for other institutes to make the classrooms comfortable.

Associations between indoor environment in residential buildings in wet and dry seasons and health of students in upper northern Thailand

We performed a repeated questionnaire study on home environment and health (six medical symptoms) in 1159 junior high school students (age 12.8±0.7years) in upper northern Thailand in wet and dry seasons. Data on outdoor temperature, relative humidity (RH), and air pollution were collected from nearest monitoring station. Odds ratios (OR) were calculated by multi-level logistic regression. Most common symptoms were rhinitis (62.5%), headache (49.8%), throat (42.8%), and ocular symptoms (42.5%). Ocular symptoms were more common at lower RH and rhinitis more common in dry season. Water leakage (28.2%), indoor mold (7.1%), mold odor (4.1%), and windowpane condensation (13.6%) were associated with all six symptoms (ORs: 1.3-3.5). Other risk factors included cat keeping, environmental tobacco smoke (ETS), other odor than mold odor, gas cooking, and cooking with biomass fire. Biomass burning inside and outside the home for other reasons than cooking was associated with all six symptoms (ORs: 1.5-2.6). Associations between home environment exposure and rhinitis were stronger in wet season. In conclusion, dampness-related exposure, windowpane condensation, cat keeping, ETS, gas cooking, and biomass burning can impair adolescent health in upper northern Thailand. In subtropical areas, environmental health effects should be investigated in wet and dry seasons.

Preliminary rolling noise measurements toward the design of a standard rolling noise device

The tapping machine has long existed as the primary standard method for measuring the performance of floors in buildings in response to structure-borne noise. However, other sources of structure-borne noise exist. One of these is rolling noise: such as a trolley rolling across the floor in an indoor building environment. Because the sound profile of indoor rolling noise is substantially different than that of impact noise, the techniques developed to reduce the latter may not necessarily be effective at reducing the former. To this end, a means of repeatably measuring indoor rolling noise is needed. Here the results of a study on indoor rolling noise are presented, identifying the various characteristics of this type of excitation which until now have been left unexplored. The proposal for a standard rolling device is also put forth: a machine which may be capable of characterizing a floor’s performance with regards to indoor rolling noise. A series of indoor rolling noise tests were conducted in order to characterize the range of sound profiles that various indoor rolling items are capable of producing, as well as identify how the different characteristics govern the shape of the sound profile produced. Just as the standard tapping machine assesses a floor’s performance in response to impact noise, a standard rolling machine may assess a floor’s performance in response to rolling noise.

Occupants’ Satisfaction toward Indoor Environment Quality of Platinum Green-Certified Office Buildings in Tropical Climate

The quality of the indoor environment has become a vital component for buildings due to the time spent indoors. To this extent, the performance of the indoor environment is considered as part of the greenery criteria by green rating schemes such as the Green Building Index in Malaysia. This study aims to investigate and assess the quality of the indoor environment of Platinum-certified office buildings in a tropical climate. This research applied a case study approach over two Platinum-certified office buildings. Post-occupancy evaluation is employed integrating full-scale measurement with an occupants’ survey. The measurement was carried out from May to August, and 112 questionnaires were retrieved to evaluate occupants’ satisfaction with aspects of the indoor environment. Thermal comfort, indoor air quality, acoustic, lighting, furniture, and cleanliness are considered as the main study variables. The findings of full-scale measurement indicated high relative humidity, and low air velocity and illuminance. While occupants reported overall indoor environment quality (IEQ) comfort, a significant correlation of variables was observed. The main sources of dissatisfaction were identified as overcooling around 24 °C, high relative humidity (RH), around 70% RH, glare, and background noise around 51.9 dB. Statistically, a significant difference between occupants’ responses to IEQ of two cases was identified, although both buildings are labelled with a Platinum certificate.

Distribution characteristics of indoor oxygen concentration under natural ventilation in oxygen-enriched buildings at high altitudes

Diffuse oxygen supply is an important means to improve the indoor oxygen environment of buildings and ensure physiological and psychological health of immigrants in plateau areas. Existing research on oxygen enrichment strategies at high altitudes has mainly focused on confined spaces under mechanical ventilation, with few studies on the distribution of indoor oxygen concentration under natural ventilation in actual buildings. This study used a verified computational fluid dynamics (CFD) method to investigate the indoor oxygen distribution with practical consideration of natural ventilation at high altitudes. The results showed that the oxygen distribution under wind-driven natural ventilation was more nonuniform than that under buoyancy-driven natural ventilation, with the ratio of local oxygen concentration to overall-mean oxygen concentration, the k value, between 0.8 and 1.3 under wind-driven natural ventilation and between 0.9 and 1.1 under buoyancy-driven natural ventilation. The effects of meteorological condition and oxygen source position on indoor spatial oxygen distribution characteristics were explored with careful examination in human occupied zone under lying, sitting and standing postures. The results can provide implications for effective and energy saving design of indoor oxygen supply system in plateau buildings.

Novel Actuator Fault Diagnosis Framework for Multizone HVAC Systems Using 2-D Convolutional Neural Networks

Heating, ventilation, and air conditioning (HVAC) systems are used to condition the indoor environment in buildings. They can be subjected to malfunctioning since they are the most extensively operated buildings’ components that account alone for almost half of the total building energy usage. Therefore, fault diagnosis (FD) of the HVAC system is important to maintain the system’s reliability and efficiency and provide preventive maintenance. This article presents a supervised FD strategy for single actuator faults in HVAC systems given that actuators, such as dampers and valves, are mostly prone to faults resulting in thermal discomfort and energy inefficiency in buildings. The proposed approach is based on 2-D convolutional neural networks (CNNs) using an efficient 1-D-to-2-D data transformation performed on the time-series signals acquired from the HVAC system. The performance of the CNNs is ensured by an optimal tuning of its significant hyperparameters using the Bayesian optimization algorithm toward maximizing the classification accuracy. The proposed 1-D-to-2-D data transformation approach is computationally efficient and eliminates the use of advanced signals preprocessing. It is performed in two schemes: the static and dynamic schemes to analyze the correlation between the system’s variables and consider the temporal effects of the time-series signals without compromising the detection time. The proposed approach is developed and validated using simulation data collected from a three-zone HVAC system simulator using Transient System Simulation Tool (TRNSYS). It demonstrates improved performance compared to the 1-D CNN-based approach and the other standard data-driven approaches for actuator FD in HVAC systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the problem of actuator faults, such as dampers and valves in heating, ventilation, and air conditioning (HVAC) systems. They are at risk of malfunctioning or failure resulting in reduced system efficiency, potential interference with supervision schemes execution, and equipment deterioration. This article presents a novel approach for single actuator faults in HVAC systems using the recently evolving topology of convolutional neural networks that are known for their high accuracy and reliable performance compared to the standard approaches. It utilizes the building historical data that can be obtained from the building management systems and without the need for either expert knowledge or details about the HVAC system. In future research, we plan to address the problem of multiple actuator faults, as well as simultaneous sensor and actuator faults.

Gap theory based performance analysis: A case study of an Indian hostel building with passive architectural character

Meeting diversified expectations of users is a constant challenge. Only limited studies examine the perception as expectation and satisfaction of hostel building indoor environments, especially in India. This identified research gap is addressed in this study to propose a systematic indicative post-occupancy evaluation method using gap theory under the SERVQUAL model. Through site visits and focus group meetings, six main performance aspects of the hostel’s facilities were identified: visual comfort, thermal comfort, aural comfort, fire safety, hygiene, and communication. Further, a perception survey had conducted for 234 users. The thermal and hygiene aspects had shown the least expectation and satisfaction, while fire safety was the highest. This gives the idea of the preconception of users on aspects. Further, the perceptions were evaluated based on user subgroups such as gender (male and female), room type (single and double), academic year (1 and > 1), and course (UG and PG). The results show that females, single rooms, academic year > 1, and PG hold expectations higher. The service gaps range from -0.09 (fire) to -1.05 (hygiene), where this gap can be reversed. Cubic regression models were used to explain the proportion of users against change in performance gap under the aspects. This could be used for benchmarking for similar projects. This methodology can be adopted for hostel buildings for perception studies.

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO.

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

Contact-less PLC: channel analysis and measurements campaign

In the indoor environment of multiple-story buildings, the line-of-sight (LOS) radio frequency (RF) signal is generally obstructed by walls and slabs. As a solution to this dilemma, power line communication (PLC) is an alternative to overcome the LOS RF penetration issue. PLC is a cost-effective system that uses an alternative technology approach, through the help of already existing electrical distribution network in-side the building, to transmit data. In this approach, the electrical wire acts as a radiating antenna which takes through the RF signal. Firstly, the signal is detected and demodulated by an RF receiver, which becomes mobile due to the contactless aspect of the link. The focus of this paper is to analyze the performance of such a system and propose measurement results. The overall concatenation of PLC and RF systems. Simulation results exhibit the channel frequency response of the cascaded PLC-RF and its performance is measured through practical implementation.

Intelligent task scheduling of distributed wireless sensor network to monitor building environment

A wireless sensor network (WSN) is constructed based on the distributed cyber‐physical system for the indoor environment of smart buildings. The task allocation strategy in WSN is designed by considering computable complexity and dynamic scheduling for the dynamic task scheduling problem in the smart building. First, each task is divided as several subtasks which are calculated by the appropriate computing nodes. Then, the sub‐tasks are allocated according to a task scheduling sequence table which is constructed by the scheduling priority. The tasks are processed in the order in task scheduling sequence table. The simulations show that the proposed task scheduling can effectively reduce the communication time and waiting time of tasks in the distributed and computable WSN in the intelligent building indoor environment. Meanwhile, it can also improve the success rate of task scheduling. Finally, the proposed task scheduling can optimize the operating efficiency of the smart building system.

A disturbance compensation enhanced control strategy of HVAC systems for improved building indoor environment control when providing power grid frequency regulation

Renewable electricity generations are promising to address the global energy issue while they also place great pressure on the reliability of power grids due to their intermittent nature. In recent years, existing heating, ventilation and air-conditioning (HVAC) systems in buildings have attracted increasing attention to implement continuous demand response in providing frequency regulation service, which can enhance instantaneous power balance and reliability of power grids without extra huge investment. When providing frequency regulation service, the power use of HVAC systems would follow the regulation signals. On the other hand, these signals, acting as continuous disturbances, affect naturally the building indoor environment control at the demand side. In this paper, a novel control strategy is proposed, which can prevent the sacrifice of the building indoor environment when providing the service. The core element of this control strategy is a frequency disturbance compensation scheme, which is developed based on the concept of “disturbance-observer-based control”. Experimental results show that the use of the proposed strategy can achieve significant improvement in the building indoor environment control without sacrificing the quality of frequency regulation service. In addition, the wear level of the valve was not affected significantly when adopting the frequency disturbance compensation scheme.

Winter air infiltration induced by combined buoyancy and wind forces in large-space buildings

Excessive winter air infiltration causes high energy consumption for space heating and unsatisfactory indoor environment in large-space buildings, which has not been fully considered in the design and operation stages. This study proposes a simplified method to predict winter air infiltration rate in large-space buildings induced by combined buoyancy and wind forces. The method is based on a theoretical model of indoor space and a CFD simulation of outdoor wind. Compared with a full-scale indoor-outdoor CFD model, the simplified method predicts the air infiltration rate with an average absolute deviation of 5.4% (max: 11.0%). The simplified method is then utilized to discuss the differences between winter air infiltration and natural ventilation in large-space buildings. The average indoor-outdoor temperature difference is only 2.2~4.8 ​K in the natural ventilation condition, while that reaches 10.3~31.6 ​K in the space heating condition. The effective opening area in the natural ventilation condition is 3.5~3.9 times of that in the space heating condition. Therefore, the dominant driven force of buoyancy in the space heating condition results in little possibility of solution multiplicity for winter air infiltration, which ensures the validity of the proposed method.

The Study on Thickness Detection Technology of Reflective Thermal Insulation Coatings for Buildings Based on Hyperspectral Technology

As a new type of building coatings, reflective heat insulation coatings have been widely used in building construction by virtue of its advantages of energy saving and environmental protection. The high and low performance of reflective heat insulation coatings directly affects the performance of building energy saving and environmental protection, and has a great impact on the indoor environment of buildings. Reflective thermal insulation coatings for buildings mainly achieve energy saving and environmental protection by reflecting and absorbing solar radiation(visible-near infrared) and building radiation(thermal infrared). For specific building reflective heat insulation coatings, the interaction between them and light mainly depends on the construction parameters, such as coating thickness. Therefore, hyperspectral technology is used to quantitatively analyze the reflection and absorption characteristics of building reflective heat insulation coatings, and to study the influence of coating construction parameters(thickness) on the performance of building reflective heat insulation coatings, so as to provide scientific and technological support for coating construction detection. With the help of hyperspectral technology, this study measured the spectral data of different coatings thickness, analyzed the evolution law of the spectral characteristics of coatings with the increase of the thickness of coatings, studied the coatings index which can characterize the thickness of coatings construction, and analyzed the correlation between the spectral data of coatings and the coatings index constructed by them and the thickness of coatings respectively. Selecting and screening the sensitive indicators of coatings construction thickness, building the thickness detection model of coatings construction, and searching for the method suitable for the thickness detection of coatings construction. The results show that:(1) the spectral data located in the 420～1 070 nm range are sensitive to the thickness of coatings 0.1～2.5 mm, and the correlation coefficient r with the thickness of coatings construction is high and phase-wise. For stability, it shows that the spectral range is sensitive to the thickness of coatings and can be used to detect the thickness of coatings;(2) Compared with the original spectrum, the coating index can effectively enhance the sensitivity of the spectrum to the thickness of coatings, and the RCI index constructed from 484 and 479 nm is the best parameter to characterize the thickness of coatings in the five categories of coatings index;(3) Among the five kinds of coatings indices, the model based on RCI index has the highest accuracy and is the best one, and its R~2=0.973, RMSE=0.185, RPD=4.018.

Indoor air quality monitoring and human perception survey on air quality in public buildings in Timisoara

The paper presents a study aimed at investigating the indoor air quality (IAQ) and people’s perception regarding the indoor environment of schools and public buildings in Timisoara. The indoor air quality monitoring was performed during the cold season, in the period January 2020 – February 2021. Six public buildings – schools and administrative edifices belonging to the municipality were subject of monitoring. An Andersen microbial air sampler was used to enumerate the numbers and different kinds of respirable bacteria and fungi inside of the selected classrooms and offices. The results of the specialized laboratory analyses indicate that the microbiological contaminant counts, expressed in colonyforming unit (CFU/m3), fall within the permissible limits for all monitored rooms. No beta-hemolytic streptococci or Staphylococcus aureus were identified, conditionally pathogenic bacteria for the upper respiratory tract. Most moulds were of the genus Penicillium, without having a pathogenic significance for humans. Occupants’ perception on indoor air quality, thermal comfort, health complaints and symptoms such allergies or respiratory illnesses with possible connection to the Sick building syndrome were surveyed by questionnaire. The findings were analysed and a plan to improve IAQ in the public buildings was proposed, with specific measures to increase the comfort and health of the learning and working environment.

Sparse and Low-Overlapping Point Cloud Registration Network for Indoor Building Environments

AbstractRegistration aims at merging multiple scans to cover all scenes of a large environment. Thus, it is crucial to many civil infrastructure applications based on three-dimensional (3D) models....

Performance assessment of contemporary energy-optimized office buildings under the impact of climate change

Climate change can significantly impact the total energy consumption and indoor environment of buildings. This study investigates a potential adaptation and mitigation pathway by implementing a multi-objective optimization strategy to minimize the impact. A baseline building model was carefully established. A sampling-based approach and a multi-objective optimization method were combined to enlarge the vision of this study. Performance of a baseline model, baseline variants, and optimized models under different climate change scenarios and time frames were compared. The results reveal that the present building model will experience a 7.2%–12.3% increase in total energy consumption, along with a significantly longer overheating period in the future. Optimal models show considerable superiority in energy savings and overheating prevention compared to the baseline model and its variants; but under future climate conditions, none of them can help maintain both energy consumption and overheating at the present level of the baseline building. This study recommends that simultaneous evaluation of multiple performance metrics helps make a more accurate and comprehensive assessment of climate change's impact on buildings.