Pub Date : 2023-02-28DOI: 10.1080/19401493.2023.2183259
Juanli Guo, Jian Zhou, Mingchen Li, Siao Lu
The number of studies considering building performance optimization (BPO) in the building design phase is steadily growing, but many of the existing studies do not consider the applicability of many-objective optimization algorithms when increasing the objective dimensions. This article first compares the NSGA-II, IDBEA, MSOPS-II, and NSGA-III algorithms. Then, the algorithm most suitable for many-objective optimization is combined with Artificial natural network(ANN) and TOPSIS-AHP to complete the optimization of four dimensions of building energy consumption (EC), useful daylight illuminance (UDI), comfort time ratio (CTR) and energy-saving envelope cost (ESEC) for village houses in cold regions of China. The results show that the NSGA-III algorithm performs well in terms of convergence speed, convergence, diversity, and uniformity when solving many-objective problems compared to the other three algorithms. Finally, four optimization strategies were selected using the TOPSIS-AHP method.
{"title":"Based on ANN and many-objective optimization to improve the performance and economy of village houses in Chinese cold regions","authors":"Juanli Guo, Jian Zhou, Mingchen Li, Siao Lu","doi":"10.1080/19401493.2023.2183259","DOIUrl":"https://doi.org/10.1080/19401493.2023.2183259","url":null,"abstract":"The number of studies considering building performance optimization (BPO) in the building design phase is steadily growing, but many of the existing studies do not consider the applicability of many-objective optimization algorithms when increasing the objective dimensions. This article first compares the NSGA-II, IDBEA, MSOPS-II, and NSGA-III algorithms. Then, the algorithm most suitable for many-objective optimization is combined with Artificial natural network(ANN) and TOPSIS-AHP to complete the optimization of four dimensions of building energy consumption (EC), useful daylight illuminance (UDI), comfort time ratio (CTR) and energy-saving envelope cost (ESEC) for village houses in cold regions of China. The results show that the NSGA-III algorithm performs well in terms of convergence speed, convergence, diversity, and uniformity when solving many-objective problems compared to the other three algorithms. Finally, four optimization strategies were selected using the TOPSIS-AHP method.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"72 1","pages":"526 - 536"},"PeriodicalIF":2.5,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87436040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-24DOI: 10.1080/19401493.2023.2180538
Zhujing Zhang, K. Kircher, Yuan Cai, Jonathon G. Brearley, David Birge, L. Norford
ABSTRACT Heatwaves are becoming more frequent and severe, intensifying cooling demand and reducing air conditioner efficiencies. This causes peaks in electricity demand that pose operational challenges to power grids. This paper provides methods to mitigate demand peaks and heat stress under heatwaves by jointly adjusting fan speeds and thermostat setpoints in buildings. The methods involve (1) learning baseline models to predict load and thermal comfort, (2) fitting perturbation models that relate fan speed and thermostat setpoint adjustments to perturbations in load and thermal comfort, and (3) optimizing peak load and thermal comfort. The methods are implementable in real buildings, providing fast, accurately predicted optimized solutions that flatten demand peaks and mitigate personal heat stress. This paper demonstrates the methodology through simulation-based case studies of a single building and a six-building neighbourhood. In case studies, the methods reduce peak load by 8–10% while maintaining occupants' thermal comfort within safe and comfortable ranges. Highlights This paper develops data-driven methods to reduce peak demand and mitigate heat stress during heatwaves. The methods are designed for straightforward implementation in the field. In case studies, the methods reduce peak demand by 8–10% while maintaining thermal comfort within safe and comfortable ranges. To achieve the same level of peak load reduction, jointly adjusting fan speed, rather than solely thermostat setpoint, improves thermal comfort by 5% in the test case.
{"title":"Mitigating peak load and heat stress under heatwaves by optimizing adjustments of fan speed and thermostat setpoint","authors":"Zhujing Zhang, K. Kircher, Yuan Cai, Jonathon G. Brearley, David Birge, L. Norford","doi":"10.1080/19401493.2023.2180538","DOIUrl":"https://doi.org/10.1080/19401493.2023.2180538","url":null,"abstract":"ABSTRACT Heatwaves are becoming more frequent and severe, intensifying cooling demand and reducing air conditioner efficiencies. This causes peaks in electricity demand that pose operational challenges to power grids. This paper provides methods to mitigate demand peaks and heat stress under heatwaves by jointly adjusting fan speeds and thermostat setpoints in buildings. The methods involve (1) learning baseline models to predict load and thermal comfort, (2) fitting perturbation models that relate fan speed and thermostat setpoint adjustments to perturbations in load and thermal comfort, and (3) optimizing peak load and thermal comfort. The methods are implementable in real buildings, providing fast, accurately predicted optimized solutions that flatten demand peaks and mitigate personal heat stress. This paper demonstrates the methodology through simulation-based case studies of a single building and a six-building neighbourhood. In case studies, the methods reduce peak load by 8–10% while maintaining occupants' thermal comfort within safe and comfortable ranges. Highlights This paper develops data-driven methods to reduce peak demand and mitigate heat stress during heatwaves. The methods are designed for straightforward implementation in the field. In case studies, the methods reduce peak demand by 8–10% while maintaining thermal comfort within safe and comfortable ranges. To achieve the same level of peak load reduction, jointly adjusting fan speed, rather than solely thermostat setpoint, improves thermal comfort by 5% in the test case.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"11 1","pages":"493 - 506"},"PeriodicalIF":2.5,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90464435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT To simulate the indoor air distribution accurately and quickly, this paper proposed a turbulence calculation model based on the zero-equation model and the SUPG finite element method. The optimal calculation parameters were investigated. The effects of air outlet positions, Reynolds numbers, and obstacle positions on indoor air distribution were studied. The results show that the ranges of Reynolds numbers which satisfy the summer and winter demands of indoor air velocity as the outlet at the right down position are larger than those when the outlet locates left down. When the air outlet locates at the left down position, the velocity non-uniformity coefficients are less than those under the other two conditions. Regardless of whether the air outlet is at the left or right position, the obstacle in the middle of a room can lead to worse velocity uniformity when air velocities in the working zone can meet the velocity demand.
{"title":"Factors affecting air distribution in air conditioning air supply room based on SUPG finite element and zero equation","authors":"Zhen Miao, Zhendi Ma, Qiong-xiang Kong, Yaolin Jiang","doi":"10.1080/19401493.2023.2177731","DOIUrl":"https://doi.org/10.1080/19401493.2023.2177731","url":null,"abstract":"ABSTRACT To simulate the indoor air distribution accurately and quickly, this paper proposed a turbulence calculation model based on the zero-equation model and the SUPG finite element method. The optimal calculation parameters were investigated. The effects of air outlet positions, Reynolds numbers, and obstacle positions on indoor air distribution were studied. The results show that the ranges of Reynolds numbers which satisfy the summer and winter demands of indoor air velocity as the outlet at the right down position are larger than those when the outlet locates left down. When the air outlet locates at the left down position, the velocity non-uniformity coefficients are less than those under the other two conditions. Regardless of whether the air outlet is at the left or right position, the obstacle in the middle of a room can lead to worse velocity uniformity when air velocities in the working zone can meet the velocity demand.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"142 1","pages":"460 - 476"},"PeriodicalIF":2.5,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77535681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-14DOI: 10.1080/19401493.2023.2179115
Chen Chen, Lup Wai Chew, C. Gorlé
ABSTRACT Night-time passive cooling is an energy-efficient cooling strategy, but the design of passive cooling systems relies on imperfect computational models, which require validation. This paper assesses the importance of spatial variability in the temperature field when performing model validation. Full-scale temperature measurements in a three story atrium building reveal spatial variability of up to on each floor during the natural ventilation process. Validation of a dynamic thermal model with uncertainty quantification reveals accurate volume-averaged air temperature predictions. Discrepancies are on the order of the sensor accuracy ( ), and are primarily due to slightly under-predicted cooling rates in the model. Importantly, this trend would be identified incorrectly when validating the model against the building's built-in sensors, which consistently record 0.05–1.63 higher temperatures than the volume-averaged air temperature. These findings highlight the importance of spatial variability and careful temperature sensor placement in naturally ventilated buildings.
{"title":"Characterizing spatial variability in the temperature field to support thermal model validation in a naturally ventilated building","authors":"Chen Chen, Lup Wai Chew, C. Gorlé","doi":"10.1080/19401493.2023.2179115","DOIUrl":"https://doi.org/10.1080/19401493.2023.2179115","url":null,"abstract":"ABSTRACT Night-time passive cooling is an energy-efficient cooling strategy, but the design of passive cooling systems relies on imperfect computational models, which require validation. This paper assesses the importance of spatial variability in the temperature field when performing model validation. Full-scale temperature measurements in a three story atrium building reveal spatial variability of up to on each floor during the natural ventilation process. Validation of a dynamic thermal model with uncertainty quantification reveals accurate volume-averaged air temperature predictions. Discrepancies are on the order of the sensor accuracy ( ), and are primarily due to slightly under-predicted cooling rates in the model. Importantly, this trend would be identified incorrectly when validating the model against the building's built-in sensors, which consistently record 0.05–1.63 higher temperatures than the volume-averaged air temperature. These findings highlight the importance of spatial variability and careful temperature sensor placement in naturally ventilated buildings.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"1 1","pages":"477 - 492"},"PeriodicalIF":2.5,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90973513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.1080/19401493.2022.2163422
Hanieh Nourkojouri, Arman Nikkhah Dehnavi, Sheida Bahadori, M. Tahsildoost
ABSTRACT As a critical factor in architectural design, emergency evacuation is influenced by numerous parameters. Designers utilize modelling software to evaluate their sketches after completion ofbasic design. However, no various alternatives of early design stages could be assessed via simulations, since it is a time-consuming procedure. In this study, deep-learning algorithms have been adapted for the assessment of the evacuation process at early design stages. The main methods applied include an image-to-image translation with a conditional GAN (Pix2Pix) and Extreme Gradient Boosting (XGBoost). The developed Pix2Pix model generates the heat maps of possible route congestions with a Structural Similarity Index (SSIM) of 0.89. Besides, the XGBoost model predicts the evacuation time with the mean absolute error (MAE) and R2 values of 36 s and 0.94, respectively. This method generates the results of intended analyses at high speed and is a reliable alternative for time-consuming evacuation simulations in early design stages.
{"title":"Early design stage evaluation of architectural factors in fire emergency evacuation of the buildings using Pix2Pix and explainable XGBoost model","authors":"Hanieh Nourkojouri, Arman Nikkhah Dehnavi, Sheida Bahadori, M. Tahsildoost","doi":"10.1080/19401493.2022.2163422","DOIUrl":"https://doi.org/10.1080/19401493.2022.2163422","url":null,"abstract":"ABSTRACT As a critical factor in architectural design, emergency evacuation is influenced by numerous parameters. Designers utilize modelling software to evaluate their sketches after completion ofbasic design. However, no various alternatives of early design stages could be assessed via simulations, since it is a time-consuming procedure. In this study, deep-learning algorithms have been adapted for the assessment of the evacuation process at early design stages. The main methods applied include an image-to-image translation with a conditional GAN (Pix2Pix) and Extreme Gradient Boosting (XGBoost). The developed Pix2Pix model generates the heat maps of possible route congestions with a Structural Similarity Index (SSIM) of 0.89. Besides, the XGBoost model predicts the evacuation time with the mean absolute error (MAE) and R2 values of 36 s and 0.94, respectively. This method generates the results of intended analyses at high speed and is a reliable alternative for time-consuming evacuation simulations in early design stages.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"387 1","pages":"415 - 433"},"PeriodicalIF":2.5,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76653145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-04DOI: 10.1080/19401493.2022.2163423
Andrea Carlo D’Alicandro, A. Mauro
ABSTRACT Carbon dioxide (CO2) can reduce cognitive abilities at higher concentrations. CO2 can be used as a proxy for gas transport in an indoor environment and as an index to determine Indoor Air Quality (IAQ). In the present work, CO2 transport inside a real university classroom has been analysed experimentally and numerically. The main novelty is related to the experimental characterization of the airflow, boundary conditions for swirl diffusers and CO2 transport occurring in an actual university classroom equipped with a Turbulent Mixing Airflow (TMA) system. The numerical methodology, validated against the experimental measurements performed by the authors, has been used to identify the most suitable turbulence model for both thermo-fluid dynamic and CO2 transport simulations. Three different RANS k-ε turbulence models have been compared: the Standard k-ε, the RNG k-ε and the Realizable k-ε. Moreover, the evacuation time and the effects of turbulent diffusivity have been analysed.
{"title":"Experimental and numerical analysis of CO2 transport inside a university classroom: effects of turbulent models","authors":"Andrea Carlo D’Alicandro, A. Mauro","doi":"10.1080/19401493.2022.2163423","DOIUrl":"https://doi.org/10.1080/19401493.2022.2163423","url":null,"abstract":"ABSTRACT Carbon dioxide (CO2) can reduce cognitive abilities at higher concentrations. CO2 can be used as a proxy for gas transport in an indoor environment and as an index to determine Indoor Air Quality (IAQ). In the present work, CO2 transport inside a real university classroom has been analysed experimentally and numerically. The main novelty is related to the experimental characterization of the airflow, boundary conditions for swirl diffusers and CO2 transport occurring in an actual university classroom equipped with a Turbulent Mixing Airflow (TMA) system. The numerical methodology, validated against the experimental measurements performed by the authors, has been used to identify the most suitable turbulence model for both thermo-fluid dynamic and CO2 transport simulations. Three different RANS k-ε turbulence models have been compared: the Standard k-ε, the RNG k-ε and the Realizable k-ε. Moreover, the evacuation time and the effects of turbulent diffusivity have been analysed.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"3 1","pages":"434 - 459"},"PeriodicalIF":2.5,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75221351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1080/19401493.2022.2147674
Dilini Wickrama Achchige, Dong Chen, G. Kokogiannakis, M. Fiorentini
ABSTRACT Fixed thermostat setpoints and schedules are commonly used in residential building energy simulation and rating. While this approach is simple to implement, it does not represent occupants with varying preferences. In this study, based on field data from 102 households in three Australian cities, two alternative thermostat setting approaches were investigated. The first method (Probability Distribution Approach) uses all the values in a thermostat settings probability distribution generated from the field data. This was compared with a more straightforward method, where the thermostat settings were derived by applying weighted average thermostat settings. Both approaches were benchmarked against a series of simulations that used randomly generated thermostat settings with the same thermostat settings probability distributions. Results show that the Probability Distribution Approach matches better the benchmarking results (CV(RMSE) 1-8%) than the weighted average method (CV(RMSE) 9-37%), particularly for cooling demand.
{"title":"Probabilistic modelling of occupants’ thermostat preferences for residential building energy simulation and rating","authors":"Dilini Wickrama Achchige, Dong Chen, G. Kokogiannakis, M. Fiorentini","doi":"10.1080/19401493.2022.2147674","DOIUrl":"https://doi.org/10.1080/19401493.2022.2147674","url":null,"abstract":"ABSTRACT Fixed thermostat setpoints and schedules are commonly used in residential building energy simulation and rating. While this approach is simple to implement, it does not represent occupants with varying preferences. In this study, based on field data from 102 households in three Australian cities, two alternative thermostat setting approaches were investigated. The first method (Probability Distribution Approach) uses all the values in a thermostat settings probability distribution generated from the field data. This was compared with a more straightforward method, where the thermostat settings were derived by applying weighted average thermostat settings. Both approaches were benchmarked against a series of simulations that used randomly generated thermostat settings with the same thermostat settings probability distributions. Results show that the Probability Distribution Approach matches better the benchmarking results (CV(RMSE) 1-8%) than the weighted average method (CV(RMSE) 9-37%), particularly for cooling demand.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"1 1","pages":"398 - 414"},"PeriodicalIF":2.5,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73432899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1080/19401493.2022.2141881
Juanli Guo, Zhoupeng Wang, Mingchen Li, Yongyun Jin
ABSTRACT This study is the first to conduct a global sensitivity analysis to identify the crucial variables that have an impact on the energy consumption of substations. The peak cooling and heating energy consumption, as well as the annual cooling and heating energy consumption of a substation in Shandong, are all simulated basing the Monte Carlo method. The simulation outputs are discussed by uncertainty analysis to obtain more accurate energy consumption thresholds. Subsequently, the treed Gaussian process and the standardized rank regression coefficient are used to perform a global sensitivity analysis of the simulation outputs. The results of the two global sensitivity analyses are practically the same, demonstrating that robustness can be ensured by simultaneously using two methods based on different theories. In addition, this study provides an efficient method for the energy-saving retrofitting of the existing substation and the energy-saving design of green substations in the future.
{"title":"Uncertainty quantification and sensitivity analysis of energy consumption in substation buildings at the planning stage","authors":"Juanli Guo, Zhoupeng Wang, Mingchen Li, Yongyun Jin","doi":"10.1080/19401493.2022.2141881","DOIUrl":"https://doi.org/10.1080/19401493.2022.2141881","url":null,"abstract":"ABSTRACT This study is the first to conduct a global sensitivity analysis to identify the crucial variables that have an impact on the energy consumption of substations. The peak cooling and heating energy consumption, as well as the annual cooling and heating energy consumption of a substation in Shandong, are all simulated basing the Monte Carlo method. The simulation outputs are discussed by uncertainty analysis to obtain more accurate energy consumption thresholds. Subsequently, the treed Gaussian process and the standardized rank regression coefficient are used to perform a global sensitivity analysis of the simulation outputs. The results of the two global sensitivity analyses are practically the same, demonstrating that robustness can be ensured by simultaneously using two methods based on different theories. In addition, this study provides an efficient method for the energy-saving retrofitting of the existing substation and the energy-saving design of green substations in the future.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"174 1","pages":"327 - 345"},"PeriodicalIF":2.5,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74980094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-14DOI: 10.1080/19401493.2022.2143568
Mario Alves da Silva, Rafael de Paula Garcia, J. Carlo
Simulation-based Optimization processes (SBO) can be valuable methods in searching for efficient buildings. This study evaluates the performance of the multi-objective algorithms RBFMOpt, NSGA2, and MHACO facing the same SBO problem. The goal is to maximize thermal comfort while minimizing the energy consumption with HVAC systems for a typical Brazilian office building. We proposed a scoring method based on four algorithms’ performance metrics: hypervolume, variability, IGD+, and coverage. We also applied a Kruskal–Wallis test to determine whether the SBO process needs multiple runs to obtain the average performance of each algorithm. The results show that RBFMOpt presents the best performance, reaching a higher score, especially in situations with low budgets for the simulation and optimization process. The results also pointed out that the number of cycles for RBFMOpt impacts directly the quality of solutions, and a higher number of cycles provided better results.
{"title":"Performance assessment of RBFMOpt, NSGA2, and MHACO on the thermal and energy optimization of an office building","authors":"Mario Alves da Silva, Rafael de Paula Garcia, J. Carlo","doi":"10.1080/19401493.2022.2143568","DOIUrl":"https://doi.org/10.1080/19401493.2022.2143568","url":null,"abstract":"Simulation-based Optimization processes (SBO) can be valuable methods in searching for efficient buildings. This study evaluates the performance of the multi-objective algorithms RBFMOpt, NSGA2, and MHACO facing the same SBO problem. The goal is to maximize thermal comfort while minimizing the energy consumption with HVAC systems for a typical Brazilian office building. We proposed a scoring method based on four algorithms’ performance metrics: hypervolume, variability, IGD+, and coverage. We also applied a Kruskal–Wallis test to determine whether the SBO process needs multiple runs to obtain the average performance of each algorithm. The results show that RBFMOpt presents the best performance, reaching a higher score, especially in situations with low budgets for the simulation and optimization process. The results also pointed out that the number of cycles for RBFMOpt impacts directly the quality of solutions, and a higher number of cycles provided better results.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"16 1","pages":"366 - 380"},"PeriodicalIF":2.5,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86659313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-09DOI: 10.1080/19401493.2022.2142295
Na Luo, Xuan Luo, M. Mortezazadeh, M. Albettar, Wanni Zhang, Dongxue Zhan, L. Wang, T. Hong
{"title":"A data schema for exchanging information between urban building energy models and urban microclimate models in coupled simulations","authors":"Na Luo, Xuan Luo, M. Mortezazadeh, M. Albettar, Wanni Zhang, Dongxue Zhan, L. Wang, T. Hong","doi":"10.1080/19401493.2022.2142295","DOIUrl":"https://doi.org/10.1080/19401493.2022.2142295","url":null,"abstract":"","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"504 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75832198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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