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
Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems viaharnessing solar energy available on building envelopes. While methods to assess solar irradiation, especiallyo...Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems viaharnessing solar energy available on building envelopes. While methods to assess solar irradiation, especiallyon rooftops, are well established, the assessment on building facades usually involves a higher effort due tomore complex urban features and obstructions. The drawback of existing physics-based simulation programsare that they require significant manual modeling effort and computing time for generating time resolveddeterministic results. Yet, solar irradiation is highly intermittent and representing its inherent uncertainty maybe required for designing robust BIPV energy systems. Targeting on these drawbacks, this paper proposes adata-driven model based on Deep Generative Networks (DGN) to efficiently generate stochastic ensembles ofannual hourly solar irradiance time series on building facades with uncompromised spatiotemporal resolutionat the urban scale. The only input required are easily obtainable fisheye images as categorical shading maskscaptured from 3D models. In principle, even actual photographs of urban contexts can be utilized, given they are semantically segmented. The potential of our approach is that it may be applied as a surrogate for timeconsuming simulations, when facing lacking information (e.g., no 3D model exists), and to use the generatedstochastic time-series ensembles in robust energy systems planning. Our validations exemplify a good fidelityof the generated time series when compared to the physics-based simulator. Due to the nature of the usedDGNs, it remains an open challenge to precisely reconstruct the ground truth one-to-one for each hour of theyear. However, we consider the benefits of the approach to outweigh the shortcomings. To demonstrate themodel’s relevance for urban energy planning, we showcase its potential for generative design by parametricallyaltering characteristic features of the urban environment and producing corresponding time series on buildingfacade展开更多
Buildings could play a critical role in energy and food production while making highdensity cities more resilient.Productive facades(PFs),as flexible and multi-functional systems integrating photovoltaic(PV)and vertic...Buildings could play a critical role in energy and food production while making highdensity cities more resilient.Productive facades(PFs),as flexible and multi-functional systems integrating photovoltaic(PV)and vertical farming(VF)systems,could contribute to transforming buildings and communities from consumers to producers.This study analyses the architectural quality of the developed PF concept drawing on the findings of a web-survey conducted among experts e building professionals in Singapore.The developed design variants are compared with regards to key design aspects such as facade aesthetics,view from the inside,materialisation,ease of operation,functionality and overall architectural quality.The study also compares and discusses the results of the web-survey with the results of a previously conducted door-to-door survey among the potential users-residents of the Housing&Development Board(HDB)blocks.The findings confirm an overall acceptance of the PF concept and reveal a need for synergetic collaboration between architects/designers and other building professionals.Based on the defined PF design framework and the results of the two surveys,a series of recommendations and improved PF prototypes are proposed for further assessment and implementation in order to foster their scalability from buildings into communities and cities.展开更多
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Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems viaharnessing solar energy available on building envelopes. While methods to assess solar irradiation, especiallyon rooftops, are well established, the assessment on building facades usually involves a higher effort due tomore complex urban features and obstructions. The drawback of existing physics-based simulation programsare that they require significant manual modeling effort and computing time for generating time resolveddeterministic results. Yet, solar irradiation is highly intermittent and representing its inherent uncertainty maybe required for designing robust BIPV energy systems. Targeting on these drawbacks, this paper proposes adata-driven model based on Deep Generative Networks (DGN) to efficiently generate stochastic ensembles ofannual hourly solar irradiance time series on building facades with uncompromised spatiotemporal resolutionat the urban scale. The only input required are easily obtainable fisheye images as categorical shading maskscaptured from 3D models. In principle, even actual photographs of urban contexts can be utilized, given they are semantically segmented. The potential of our approach is that it may be applied as a surrogate for timeconsuming simulations, when facing lacking information (e.g., no 3D model exists), and to use the generatedstochastic time-series ensembles in robust energy systems planning. Our validations exemplify a good fidelityof the generated time series when compared to the physics-based simulator. Due to the nature of the usedDGNs, it remains an open challenge to precisely reconstruct the ground truth one-to-one for each hour of theyear. However, we consider the benefits of the approach to outweigh the shortcomings. To demonstrate themodel’s relevance for urban energy planning, we showcase its potential for generative design by parametricallyaltering characteristic features of the urban environment and producing corresponding time series on buildingfacade
基金This research was funded by the City Developments Limited(CDL)(R-295-000-134-720),SingaporeThe farming system and BIPV systems support were partially financed by the UNISEAL and Wiredbox(WBG(SG)Pte Ltd),respectively.
文摘Buildings could play a critical role in energy and food production while making highdensity cities more resilient.Productive facades(PFs),as flexible and multi-functional systems integrating photovoltaic(PV)and vertical farming(VF)systems,could contribute to transforming buildings and communities from consumers to producers.This study analyses the architectural quality of the developed PF concept drawing on the findings of a web-survey conducted among experts e building professionals in Singapore.The developed design variants are compared with regards to key design aspects such as facade aesthetics,view from the inside,materialisation,ease of operation,functionality and overall architectural quality.The study also compares and discusses the results of the web-survey with the results of a previously conducted door-to-door survey among the potential users-residents of the Housing&Development Board(HDB)blocks.The findings confirm an overall acceptance of the PF concept and reveal a need for synergetic collaboration between architects/designers and other building professionals.Based on the defined PF design framework and the results of the two surveys,a series of recommendations and improved PF prototypes are proposed for further assessment and implementation in order to foster their scalability from buildings into communities and cities.
文摘[目的]随着光伏、储能、新型建材及装配式建筑产业的发展,将光伏组件与屋面、墙体、遮阳等构件进行一体化设计与制造的光伏建筑一体化(Building Integrated Photovoltaic,BIPV)技术开始延伸为光伏储能建筑一体化(Building Integrated Photovoltaic and Energy Storge,BIPVES)技术。[方法]文章提出世界首个可充电水泥电池,将建筑墙体与光伏发电装置、储放电装置相融合;对设备和材料进行跨界创新,在玻璃表面打印高清晰度、高透光率花纹图案,制造高效光伏建材;研发预制式储能墙体,与各类钢结构装配式建筑体系进行结合,实现订制式生产、装配式施工,形成建筑构件与光伏、储能一体化的变革趋势。[结果]水泥基电池实现了建筑墙体具有光伏发电、储电以及供电等多种功能;新一代光伏建材可节省建筑外立面装饰材料的成本,降低建筑物碳排放;光伏和储能等可再生能源技术在建筑中的一体化集成,可取得最大化收益。[结论]新型光伏建材技术和水泥电池等新型储能技术具有发展前景,将可充电电池构件、光伏外墙板与装配式建筑墙体及预埋件进行组合集成并推广应用具有可行性。
