With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there i...With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.展开更多
<span style="font-family:Verdana;">Thermal comfort is one of the most important requirements that scientists and building designers must meet to ensure the indoor air quality knowing its importance on ...<span style="font-family:Verdana;">Thermal comfort is one of the most important requirements that scientists and building designers must meet to ensure the indoor air quality knowing its importance on productivity and the health of occupants. However, it has never been of great concern for architects and architectural historians and seldom explores it. Buildings are the large consumer of the most energy consumption (around 40% worldwide) and generate around 35% of GHGs like CO</span><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;"> that leads to extreme climate change. Hence, general and specific eco-friendly solutions in the field of building construction are required. Analysis of this study shows that air conditioning consumption can be significantly reduced thanks to the compressed earth bricks and by taking into account the climate and the orientation of the facades. However, this paper establishes viable low-cost option of building energy consumption while maintaining the thermal comfort and good indoor air quality. This work explains the effect of a single residential room orientation, by reducing </span><span style="font-family:Verdana;">the thermal amplitude, and improving the thermal phase shift in Ouagadougou</span><span style="font-family:Verdana;"> climate conditions in April. Internal temperature was modelled with 8 cardinal orientations. The result corresponds to a decrease of thermal amplitude </span><span style="font-family:Verdana;">damping greater than 4<span style="white-space:nowrap;">°</span>C between East-West and North-South sides and, with a thermal phase shift of 4</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">hours</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">30</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">minutes between the Nord and West walls.</span>展开更多
The building sector,especially residential buildings,accounts for a significant proportion of global energy consumption.Therefore,improving the energy efficiency of buildings is thus crucial.This research utilized Ene...The building sector,especially residential buildings,accounts for a significant proportion of global energy consumption.Therefore,improving the energy efficiency of buildings is thus crucial.This research utilized EnergyPlus to perform simulation analysis on standard residential prototype models and adopted the multi-dimensional comparative study to evaluate the optimization effect under different cases,using Chicago as the simulation location.This research included both active design and passive design dimensions and conducted simulation analysis on energy consumption of heating,cooling,and the whole building.The active design involved a temperature setpoint schedule comprehensively considering occupant activities,comfort,and energy-saving performance.Passive design of the thickness and thermal conductivity of different wall layers were clustered,comparing positive and negative aspects through±30%variations to ensure the effectiveness of the optimization plan.This indicates that among the various design factors,optimizing temperature setpoint can yield a larger energy-saving outcome compared with optimizing thermal conductivity and thickness.Compared to the baseline,changing the temperature setpoint in the active design based on occupant habits significantly reduces annual energy consumption by about 16%.In passive design,optimizing the wall console layer has a more significant effect when simulating changes in thermal conductivity and thickness.This conclusion can help building architects develop the most appropriate and effective solutions when designing and optimizing buildings to achieve the energy sustainability goal.展开更多
The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality ...The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals.One of the policies implemented in recent years was the Energy Performance Certificate(EPC)policy,which proposes building stock benchmarking to identify buildings that require rehabilitation.However,research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy.This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of(1)predicting a building’s,or household’s,energy needs;and(2)providing the user with optimum retrofit solutions,costs,and return on investment.The goal is to provide an open-source,easy-to-use interface that guides the user in the building retrofit process.The energy and EPC prediction models show a coefficient of determination(R2)of 0.84 and 0.79,and the optimization results for one case study EPC with a 2000€budget limit inÉvora,Portugal,show decreases of up to 60%in energy needs and return on investments of up to 7 in 3 years.展开更多
A large number of cases show that the multi-objective optimization method can significantly improve building performance.The method for multi-objective building performance optimization(BPO)design has achieved rapid d...A large number of cases show that the multi-objective optimization method can significantly improve building performance.The method for multi-objective building performance optimization(BPO)design has achieved rapid development in recent years.However,the BPO method still needs to be improved.Specifically,weak interaction between the optimization process and the decision-making process results in low optimization efficiency,which limits the widespread application of the optimization method in early design stage.In this paper,a new interactive BPO mode is explored to strengthen the interaction between the optimization process and decisionmaking process,and a preference-based multi-objective BPO method is proposed to account for designers'decision preferences during the optimization process,making the objective more controllable,improving the optimization efficiency and ensuring the diversity of solutions.Firstly,this paper illustrates the proposed method in detail,defines the concept of performance preference,expounds the flow of the preference-based multi-objective optimization algorithm,and proposes three indicators to evaluate the algorithm,which includes convergence speed,preference satisfaction rate,and diversity measurement.Secondly,through testing and comparison,it is found that the proposed preference-based algorithm has advantages over the non-preference optimization algorithm(represented by the NSGA-II algorithm).The proposed method leads to faster convergence and higher preference satisfaction,so it is more suitable for the BPO process in the early design stage.Specially,the proposed method can achieve 100%preference satisfaction rate with only 2400 simulations,while the non-preference method can only achieve 20%preference satisfaction rate after 5800 simulations.In this paper,a preference-based multi-objective BPO method is proposed to make the optimization process closely interact with the decision-making process and make the design preferences be accounted during the BPO process,thereby improving the optimization efficiency.In addition,this study first proposes two indicators to measure the quality of optimization results:preference satisfaction rate and diversity measurement.This study aims to guide the development of BPO methods towards providing high satisfaction rate and high quality optimization results.展开更多
The building parameters of Chinese solar greenhouse(CSG)directly affect the front roof lighting and indoor thermal environment.In order to obtain the optimal parameter combination,a building parameter optimization met...The building parameters of Chinese solar greenhouse(CSG)directly affect the front roof lighting and indoor thermal environment.In order to obtain the optimal parameter combination,a building parameter optimization method based on computational fluid dynamics(CFD)simulation and entropy weight method was proposed.Firstly,a three-dimensional thermal and humidity environment model of CSG was constructed considering the coupling effect of soil,crop,and back wall based on CFD.The reliability of the model was validated through experiments in a CSG of Yongqing County,Hebei Province of China.Then,the indoor air temperature rise rate,air temperature and humidity uneven coefficient,and average air temperature and humidity were selected as the evaluation indicators of CSG thermal and humidity environment.The ridge height,back wall height and the horizontal projection of back roof of CSG were selected as the three factors of the orthogonal test plan,and a three-factor and four-level plan was designed,resulting in 16 different parameter combinations.By use of CFD simulation,the thermal and humidity environment evaluation indicators under different parameter combinations were calculated.The entropy weight method was used to assign weights to the evaluation indicators,and the comprehensive evaluation indicators of CSG thermal and humidity environment were obtained based on the linear weighting principle.By comparing comprehensive evaluation indicators,the optimal combination of building parameters was obtained with a ridge height of 5.72 m,a back wall height of 3.2 m,and a horizontal projection of 2.1 m on the back roof.The research results can provide a practical and feasible method for optimizing the building parameters of CSG,and provided theoretical guidance for the structural design and optimization of CSG.展开更多
Heat gain through the buildings opaque facades significantly contributes to the energy consumption of Heating,Ventilation,and Air Conditioning(HVAC)systems.In the post-construction phase,retrofitting options for reduc...Heat gain through the buildings opaque facades significantly contributes to the energy consumption of Heating,Ventilation,and Air Conditioning(HVAC)systems.In the post-construction phase,retrofitting options for reducing façade heat gain are limited,with cool paints being the prevalent strategy.However,the efficacy of shading systems as an alternative strategy remains underexplored as existing research predominantly focuses on proof-of-concept validation,often overlooking a comprehensive assessment of shading system configurations across diverse climates and building typologies.Moreover,a comparative analysis of the performance and potential synergies between cool paints and shading systems on opaque facades is crucial to understand their actual effectiveness in real-world applications.To address these gaps,our study undertakes an extensive parametric simulation,taking into account variables such as shading configurations,cool paints with varying facade solar absorbance values,facade orientation,diverse climates,and different building typologies.The results demonstrate that the use of shading on opaque facades alone could result in a HVAC energy saving of 8-28%,while the application of cool paints(façade absorptance value of 0.2)alone could reduce the HVAC energy consumption by 10-35%.By combining the use of shading and cool paints,the HVAC energy savings are further increased by 2-5%.The findings of this study offer a novel perspective on the selection of opaque façade technologies,broadening the sustainable design and retrofit options.展开更多
Buildings are a major energy consumer and carbon emitter,therefore it is important to improve building energy efficiency to achieve our sustainable development goal.Deep reinforcement learning(DRL),as an advanced buil...Buildings are a major energy consumer and carbon emitter,therefore it is important to improve building energy efficiency to achieve our sustainable development goal.Deep reinforcement learning(DRL),as an advanced building control method,demonstrates great potential for energy efficiency optimization and improved occupant comfort.However,the performance of DRL is highly sensitive to hyper-parameters,and selecting inappropriate hyper-parameters may lead to unstable learning or even failure.This study aims to investigate the design and application of DRL in building energy system control,with a specific focus on improving the performance of DRL controllers through hyper-parameter optimization(HPO)algorithms.It also aims to provide quantitative evaluation and adaptive validation of these optimized controllers.Two widely used algorithms,deep deterministic policy gradient(DDPG)and soft actor-critic(SAC),are used in the study and their performance is evaluated in different building environments based on the BOPTEST virtual testbed.One of the focuses of the study is to compare various HPO techniques,including tree-structured Parzen estimator(TPE),covariance matrix adaptation evolution strategy(CMA-ES),and combinatorial optimization methods,to determine the efficacy of different hyper-parameter optimization methods for DRL.The study enhances HPO efficiency through parallel computation and conducts a comprehensive quantitative assessment of the optimized DRL controllers,considering factors such as reduced energy consumption and improved comfort.The results show that the HPO algorithms significantly improve the performance of the DDPG and SAC controllers.A reduction of 56.94%and 68.74%in thermal discomfort is achieved,respectively.Additionally,the study demonstrates the applicability of the HPO-based approach for enhancing DRL controller performance across diverse building environments,providing valuable insights for the design and optimization of building DRL controllers.展开更多
文摘With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.
文摘<span style="font-family:Verdana;">Thermal comfort is one of the most important requirements that scientists and building designers must meet to ensure the indoor air quality knowing its importance on productivity and the health of occupants. However, it has never been of great concern for architects and architectural historians and seldom explores it. Buildings are the large consumer of the most energy consumption (around 40% worldwide) and generate around 35% of GHGs like CO</span><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;"> that leads to extreme climate change. Hence, general and specific eco-friendly solutions in the field of building construction are required. Analysis of this study shows that air conditioning consumption can be significantly reduced thanks to the compressed earth bricks and by taking into account the climate and the orientation of the facades. However, this paper establishes viable low-cost option of building energy consumption while maintaining the thermal comfort and good indoor air quality. This work explains the effect of a single residential room orientation, by reducing </span><span style="font-family:Verdana;">the thermal amplitude, and improving the thermal phase shift in Ouagadougou</span><span style="font-family:Verdana;"> climate conditions in April. Internal temperature was modelled with 8 cardinal orientations. The result corresponds to a decrease of thermal amplitude </span><span style="font-family:Verdana;">damping greater than 4<span style="white-space:nowrap;">°</span>C between East-West and North-South sides and, with a thermal phase shift of 4</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">hours</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">30</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">minutes between the Nord and West walls.</span>
文摘The building sector,especially residential buildings,accounts for a significant proportion of global energy consumption.Therefore,improving the energy efficiency of buildings is thus crucial.This research utilized EnergyPlus to perform simulation analysis on standard residential prototype models and adopted the multi-dimensional comparative study to evaluate the optimization effect under different cases,using Chicago as the simulation location.This research included both active design and passive design dimensions and conducted simulation analysis on energy consumption of heating,cooling,and the whole building.The active design involved a temperature setpoint schedule comprehensively considering occupant activities,comfort,and energy-saving performance.Passive design of the thickness and thermal conductivity of different wall layers were clustered,comparing positive and negative aspects through±30%variations to ensure the effectiveness of the optimization plan.This indicates that among the various design factors,optimizing temperature setpoint can yield a larger energy-saving outcome compared with optimizing thermal conductivity and thickness.Compared to the baseline,changing the temperature setpoint in the active design based on occupant habits significantly reduces annual energy consumption by about 16%.In passive design,optimizing the wall console layer has a more significant effect when simulating changes in thermal conductivity and thickness.This conclusion can help building architects develop the most appropriate and effective solutions when designing and optimizing buildings to achieve the energy sustainability goal.
基金supported by Fundação para a Ciência e Tecnologia(FCT)through IN+UIDP/EEA/50009/2020-IST-ID,through CERIS UIDB/04625/2020Ph.D.grant under the contract of FCT 2021.04849.BD.Project C-TECH-Climate Driven Technologies for Low Carbon Cities,grant number POCI-01-0247-FEDER-045919,LISBOA-01-0247-FEDER-045919,co-financed by the ERDF-European Regional Development Fund through the Operational Program for Competitiveness and Internationalization-COMPETE 2020,the Lisbon Portugal Regional Operational Program-LISBOA 2020 and by the FCT under MIT Portugal Program.
文摘The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals.One of the policies implemented in recent years was the Energy Performance Certificate(EPC)policy,which proposes building stock benchmarking to identify buildings that require rehabilitation.However,research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy.This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of(1)predicting a building’s,or household’s,energy needs;and(2)providing the user with optimum retrofit solutions,costs,and return on investment.The goal is to provide an open-source,easy-to-use interface that guides the user in the building retrofit process.The energy and EPC prediction models show a coefficient of determination(R2)of 0.84 and 0.79,and the optimization results for one case study EPC with a 2000€budget limit inÉvora,Portugal,show decreases of up to 60%in energy needs and return on investments of up to 7 in 3 years.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.51825802)the China Postdoctoral Science Foundation Grant(No.2019M650408).
文摘A large number of cases show that the multi-objective optimization method can significantly improve building performance.The method for multi-objective building performance optimization(BPO)design has achieved rapid development in recent years.However,the BPO method still needs to be improved.Specifically,weak interaction between the optimization process and the decision-making process results in low optimization efficiency,which limits the widespread application of the optimization method in early design stage.In this paper,a new interactive BPO mode is explored to strengthen the interaction between the optimization process and decisionmaking process,and a preference-based multi-objective BPO method is proposed to account for designers'decision preferences during the optimization process,making the objective more controllable,improving the optimization efficiency and ensuring the diversity of solutions.Firstly,this paper illustrates the proposed method in detail,defines the concept of performance preference,expounds the flow of the preference-based multi-objective optimization algorithm,and proposes three indicators to evaluate the algorithm,which includes convergence speed,preference satisfaction rate,and diversity measurement.Secondly,through testing and comparison,it is found that the proposed preference-based algorithm has advantages over the non-preference optimization algorithm(represented by the NSGA-II algorithm).The proposed method leads to faster convergence and higher preference satisfaction,so it is more suitable for the BPO process in the early design stage.Specially,the proposed method can achieve 100%preference satisfaction rate with only 2400 simulations,while the non-preference method can only achieve 20%preference satisfaction rate after 5800 simulations.In this paper,a preference-based multi-objective BPO method is proposed to make the optimization process closely interact with the decision-making process and make the design preferences be accounted during the BPO process,thereby improving the optimization efficiency.In addition,this study first proposes two indicators to measure the quality of optimization results:preference satisfaction rate and diversity measurement.This study aims to guide the development of BPO methods towards providing high satisfaction rate and high quality optimization results.
基金support provided by Hebei Province Key Research and Development Program (Grant No.22327214D)Independent Research and Development Plan of Academy of Agricultural Planning and Engineering,Ministry of Agriculture and Rural Affairs (Grant No.SP202101).
文摘The building parameters of Chinese solar greenhouse(CSG)directly affect the front roof lighting and indoor thermal environment.In order to obtain the optimal parameter combination,a building parameter optimization method based on computational fluid dynamics(CFD)simulation and entropy weight method was proposed.Firstly,a three-dimensional thermal and humidity environment model of CSG was constructed considering the coupling effect of soil,crop,and back wall based on CFD.The reliability of the model was validated through experiments in a CSG of Yongqing County,Hebei Province of China.Then,the indoor air temperature rise rate,air temperature and humidity uneven coefficient,and average air temperature and humidity were selected as the evaluation indicators of CSG thermal and humidity environment.The ridge height,back wall height and the horizontal projection of back roof of CSG were selected as the three factors of the orthogonal test plan,and a three-factor and four-level plan was designed,resulting in 16 different parameter combinations.By use of CFD simulation,the thermal and humidity environment evaluation indicators under different parameter combinations were calculated.The entropy weight method was used to assign weights to the evaluation indicators,and the comprehensive evaluation indicators of CSG thermal and humidity environment were obtained based on the linear weighting principle.By comparing comprehensive evaluation indicators,the optimal combination of building parameters was obtained with a ridge height of 5.72 m,a back wall height of 3.2 m,and a horizontal projection of 2.1 m on the back roof.The research results can provide a practical and feasible method for optimizing the building parameters of CSG,and provided theoretical guidance for the structural design and optimization of CSG.
基金supported by the Singapore Min-istry of Education under grant no A-0008302-02-00 and A-8000136-01-00.
文摘Heat gain through the buildings opaque facades significantly contributes to the energy consumption of Heating,Ventilation,and Air Conditioning(HVAC)systems.In the post-construction phase,retrofitting options for reducing façade heat gain are limited,with cool paints being the prevalent strategy.However,the efficacy of shading systems as an alternative strategy remains underexplored as existing research predominantly focuses on proof-of-concept validation,often overlooking a comprehensive assessment of shading system configurations across diverse climates and building typologies.Moreover,a comparative analysis of the performance and potential synergies between cool paints and shading systems on opaque facades is crucial to understand their actual effectiveness in real-world applications.To address these gaps,our study undertakes an extensive parametric simulation,taking into account variables such as shading configurations,cool paints with varying facade solar absorbance values,facade orientation,diverse climates,and different building typologies.The results demonstrate that the use of shading on opaque facades alone could result in a HVAC energy saving of 8-28%,while the application of cool paints(façade absorptance value of 0.2)alone could reduce the HVAC energy consumption by 10-35%.By combining the use of shading and cool paints,the HVAC energy savings are further increased by 2-5%.The findings of this study offer a novel perspective on the selection of opaque façade technologies,broadening the sustainable design and retrofit options.
基金supported by the National Natural Science Foundation of China(No.72371072)Jiangsu Association for Science&Technology Youth Science&Technology Talents Lifting Project(No.JSTJ-2023-JS001).
文摘Buildings are a major energy consumer and carbon emitter,therefore it is important to improve building energy efficiency to achieve our sustainable development goal.Deep reinforcement learning(DRL),as an advanced building control method,demonstrates great potential for energy efficiency optimization and improved occupant comfort.However,the performance of DRL is highly sensitive to hyper-parameters,and selecting inappropriate hyper-parameters may lead to unstable learning or even failure.This study aims to investigate the design and application of DRL in building energy system control,with a specific focus on improving the performance of DRL controllers through hyper-parameter optimization(HPO)algorithms.It also aims to provide quantitative evaluation and adaptive validation of these optimized controllers.Two widely used algorithms,deep deterministic policy gradient(DDPG)and soft actor-critic(SAC),are used in the study and their performance is evaluated in different building environments based on the BOPTEST virtual testbed.One of the focuses of the study is to compare various HPO techniques,including tree-structured Parzen estimator(TPE),covariance matrix adaptation evolution strategy(CMA-ES),and combinatorial optimization methods,to determine the efficacy of different hyper-parameter optimization methods for DRL.The study enhances HPO efficiency through parallel computation and conducts a comprehensive quantitative assessment of the optimized DRL controllers,considering factors such as reduced energy consumption and improved comfort.The results show that the HPO algorithms significantly improve the performance of the DDPG and SAC controllers.A reduction of 56.94%and 68.74%in thermal discomfort is achieved,respectively.Additionally,the study demonstrates the applicability of the HPO-based approach for enhancing DRL controller performance across diverse building environments,providing valuable insights for the design and optimization of building DRL controllers.
