Architects face a major challenge in designing buildings that enhance human comfort while minimizing energy consumption.To address this,the present work presents a novel multi-objective optimization approach,aiming to...Architects face a major challenge in designing buildings that enhance human comfort while minimizing energy consumption.To address this,the present work presents a novel multi-objective optimization approach,aiming to determine the optimal building envelope design.The developed approach focuses on minimizing energy consumption for both heating and cooling demand.Therefore,the methodology follows three basic steps,namely,(i)energy simulation using EnergyPlus software,(ii)optimization(non-dominant sorting genetic algorithm NSGA II),and(iii)multi-criteria decision analysis(MCDA).In this study,simulations were firstly carried out on a single-story family house located in three distinct French climates:Nancy(semi-continental climate),Embrun(oceanic climate),and Nice(Mediterranean climate)in order to analyze the effect of climate on optimization results.For each site,seven decision variables for optimization problem are considered,including,wall insulation material properties,building rotation,glazing type,window-to-wall ratio(WWR),heating and cooling set-point temperatures,and infiltration rate.Pareto fronts are used to display the optimization results,which are obtained through NSGA II iterations depending on the specified optimization objectives of heating and cooling demand.The optimization(after 900 iterations)yielded 10 Pareto solutions for Nancy,26 for Embrun,and 23 for Nice.The top-ranked Pareto-optimal solution,considering the requirements of the French building code(RT2012/RE2020),Passivhaus standard,and client preferences,which minimizes cooling,heating,and total energy consumption,was selected using a new MCDA method called ranking with multiple reference profiles(RMP).The results showed that when equal weight is given to the heating and cooling objective functions,total energy consumption decreases by 8.71%for Nancy,by 9.81%to 18.34%for Embrun,and by 6.52%to 21.7%for Nice,compared to the initial design,depending on the adopted lexicographic order.展开更多
Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment.This p...Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment.This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization.The method is called BIM-AITIZATION referring to the integration of BIM data,AI techniques,and automation principles.It integrates photogrammetric data into practical BIM parameters.In addition,it enhances the precision and reliability of PV energy prediction by using artificial intelligence strategies.The primary aim of this approach is to offer advanced,data-driven energy forecasts and BIPV decarbonization while fully automating the underlying process.To achieve this,the first step is to capture point cloud data of the building through photogrammetric acquisition.This data undergoes preprocessing to identify and remove unwanted points,followed by plan segmentation to extract the plan facade.After that,a meteorological dataset is assembled,incorporating various attributes that influence energy production,including solar irradiance parameters as well as BIM parameters.Finally,machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire process.Extensive experiments are conducted,including multiple tests aimed at assessing the performance of diverse machine learning models.The objective is to identify the most suitable model for our specific application.Furthermore,a comparative analysis is undertaken,comparing the performance of the proposed model against that of various established BIPV software tools.The outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and precision.To extend its applicability,the approach is evaluated using a building case study,demonstrating its ability to generalize effectively to new building data.展开更多
Finance,supply chains,healthcare,and energy have an increasing demand for secure transactions and data exchange.Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participa...Finance,supply chains,healthcare,and energy have an increasing demand for secure transactions and data exchange.Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value.One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance(PBFT),which tolerates up to one-third of Byzantine nodes,performs within partially synchronous systems,and has superior throughput compared to other protocols.It has,however,an important bandwidth consumption:2N(N-1)messages are exchanged in a system composed of𝑁nodes to validate only one block.It is possible to reduce the number of consensus participants by restricting the validation process to nodes that have demonstrated high levels of security,rapidity,and availability.In this paper,we propose the first database that traces the behavior of nodes within a system that performs PBFT consensus.It reflects their level of security,rapidity,and availability throughout the consensus.We first investigate different Single-Task Learning(STL)techniques to classify the nodes within our dataset.Then,using Multi-Task Learning(MTL)techniques,the results are much more interesting,with classification accuracies over 98%.Integrating node classification as a preliminary step to the PBFT protocol optimizes the consensus.In the best cases,it is able to reduce the latency by up to 94%and the communication traffic by up to 99%.展开更多
文摘Architects face a major challenge in designing buildings that enhance human comfort while minimizing energy consumption.To address this,the present work presents a novel multi-objective optimization approach,aiming to determine the optimal building envelope design.The developed approach focuses on minimizing energy consumption for both heating and cooling demand.Therefore,the methodology follows three basic steps,namely,(i)energy simulation using EnergyPlus software,(ii)optimization(non-dominant sorting genetic algorithm NSGA II),and(iii)multi-criteria decision analysis(MCDA).In this study,simulations were firstly carried out on a single-story family house located in three distinct French climates:Nancy(semi-continental climate),Embrun(oceanic climate),and Nice(Mediterranean climate)in order to analyze the effect of climate on optimization results.For each site,seven decision variables for optimization problem are considered,including,wall insulation material properties,building rotation,glazing type,window-to-wall ratio(WWR),heating and cooling set-point temperatures,and infiltration rate.Pareto fronts are used to display the optimization results,which are obtained through NSGA II iterations depending on the specified optimization objectives of heating and cooling demand.The optimization(after 900 iterations)yielded 10 Pareto solutions for Nancy,26 for Embrun,and 23 for Nice.The top-ranked Pareto-optimal solution,considering the requirements of the French building code(RT2012/RE2020),Passivhaus standard,and client preferences,which minimizes cooling,heating,and total energy consumption,was selected using a new MCDA method called ranking with multiple reference profiles(RMP).The results showed that when equal weight is given to the heating and cooling objective functions,total energy consumption decreases by 8.71%for Nancy,by 9.81%to 18.34%for Embrun,and by 6.52%to 21.7%for Nice,compared to the initial design,depending on the adopted lexicographic order.
基金This work was supported by CESI EST and the GRAND EST region.The authors are very grateful to Mourad ZGHAL for fruitful discussions and Benoit DESTENAY(Teacher&responsible in charge of education at CESI school of engineering),Pierre BALLESTER,Cemal OCAKTAN,Oussama OUSSOUS and SOW Mame-Cheikh for technical assistance.The authors are grateful to GBAGUIDI HAORE Sevi(Teacher&responsible in charge of education at CESI school of engineering)and energy expert for his excellent technical support on the subject of the energy decarbonization of buildings.We would like to thank Ophéa-Eurométropole Habitat Strasbourg for allowing us to have the energy production data for these buildings.
文摘Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment.This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization.The method is called BIM-AITIZATION referring to the integration of BIM data,AI techniques,and automation principles.It integrates photogrammetric data into practical BIM parameters.In addition,it enhances the precision and reliability of PV energy prediction by using artificial intelligence strategies.The primary aim of this approach is to offer advanced,data-driven energy forecasts and BIPV decarbonization while fully automating the underlying process.To achieve this,the first step is to capture point cloud data of the building through photogrammetric acquisition.This data undergoes preprocessing to identify and remove unwanted points,followed by plan segmentation to extract the plan facade.After that,a meteorological dataset is assembled,incorporating various attributes that influence energy production,including solar irradiance parameters as well as BIM parameters.Finally,machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire process.Extensive experiments are conducted,including multiple tests aimed at assessing the performance of diverse machine learning models.The objective is to identify the most suitable model for our specific application.Furthermore,a comparative analysis is undertaken,comparing the performance of the proposed model against that of various established BIPV software tools.The outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and precision.To extend its applicability,the approach is evaluated using a building case study,demonstrating its ability to generalize effectively to new building data.
文摘Finance,supply chains,healthcare,and energy have an increasing demand for secure transactions and data exchange.Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value.One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance(PBFT),which tolerates up to one-third of Byzantine nodes,performs within partially synchronous systems,and has superior throughput compared to other protocols.It has,however,an important bandwidth consumption:2N(N-1)messages are exchanged in a system composed of𝑁nodes to validate only one block.It is possible to reduce the number of consensus participants by restricting the validation process to nodes that have demonstrated high levels of security,rapidity,and availability.In this paper,we propose the first database that traces the behavior of nodes within a system that performs PBFT consensus.It reflects their level of security,rapidity,and availability throughout the consensus.We first investigate different Single-Task Learning(STL)techniques to classify the nodes within our dataset.Then,using Multi-Task Learning(MTL)techniques,the results are much more interesting,with classification accuracies over 98%.Integrating node classification as a preliminary step to the PBFT protocol optimizes the consensus.In the best cases,it is able to reduce the latency by up to 94%and the communication traffic by up to 99%.
