[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124283-en":3,"doc-seo-124283-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124283,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Exploiting building information modeling and machine learning for optimizing rooftop photovoltaic systems - Research overview","The primary objective of this study is to develop a strategy to maximize the potential of Building Applied Photovoltaics (BAPV) by providing researchers and experts with practical tools. Operational data from a roof-mounted BAPV system are combined with Building Information Modeling (BIM) to support design and integration. A three-phase workflow covers data collection and performance assessment, BIM modeling, and machine-learning energy forecasting. The 160 kWp case study in Abruzzo (Italy) uses IEC 61724 metrics and evaluates four algorithms, finding random forest best after hyperparameter tuning.","Energy & Buildings 313 (2024) 114250  \nContents lists available at ScienceDirect Energy & Buildings  \njournal [homepage: www.elsevier.com/locate/enb](homepage: www.elsevier.com/locate/enb)  \n| Exploiting building information modeling and machine learning for optimizing rooftop photovoltaic systems\u003Cbr>*\u003Cbr>Gianni Di Giovanni , Marianna Rotilio , Letizia Giusti , Muhammad Ehtsham\u003Cbr>Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, 67100 L’Aquila, Italy |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Renewable Energy BAPV Photovoltaics (PV)\u003Cbr>Building Information Modeling (BIM) IEC-61724\u003Cbr>Machine Learning |  | The primary objective of this study is to develop a strategy to maximize the potential of Building Applied Photovoltaics (BAPV) by providing researchers and experts in the field with the appropriate tools. By utilizing operational data from an operational roof-mounted BAPV system and incorporating Building Information Modeling (BIM) to improve its design and smooth integration into built settings, the study offers novel insights. A three-phase research methodology is applied, encompassing data collection and performance assessment, BIM modeling, and machine learning algorithms for energy forecasting. The case study involves a 160 kWp photovoltaic system in Abruzzo, Italy, over a three-year period. The research employs different performance metrics recommended by IEC 61724 to compare experimental and theoretical data. BIM simulations are exploited as they are crucial for identifying the correct design process. Furthermore, four cutting-edge machine learning algorithms are used to forecast daily energy production. The random forest is identified as the best model for forecasting and the effect of tuning of hyperparameters on the efficiency of all models is also reported.\u003Cbr>The research adds to the larger conversation on sustainable energy management and solutions by providing researchers involved in the design and improvement of BAPV systems with a solid foundation for future developments. |\n\n1. Introduction  \nGlobal emissions and energy consumption of buildings continue to increase driven by strong urbanization and rapid population growth. By 2035, global energy consumption is expected to increase by 50 % compared to 1990 [1]. The three main areas of consumption identified by the most recent world energy consumption reports are industry, transportation and buildings [2]. The challenge ahead is to respond to this ever-increasing demand for energy with new measures that are sustainable for the environment, the economy and society. The implementation of Renewable Energy Sources (RES) systems in buildings isone way to respond to the need to increase their energy performance [3], facilitate the energy transition and make cities more sustainable.  \nPhotovoltaic (PV) technologies are among the most promising candidates among renewable energies and worthy substitutes for fossil resources [4]. Effectivity of PV based technologies can be further enhanced by applying and integrating photovoltaic modules into the building envelope in order to avoid further land consumption [5]. However, a correct design methodology is needed to capture all aspects involved in photovoltaic design for buildings. One of the main  \nchallenges is the optimization and modelling of the photovoltaic system on building surfaces, and Building Information Modeling (BIM) has become essential in the design process in recent years [6].  \nThe optimal exploitation of the photovoltaic potential is currently a key issue for the energy transition and proper design and installation is necessary. This necessitates thorough planning utilizing appropriate tools, which can be difficult to identify, as the existing literature shows [7–9]. BIM allows the integration of solar energy generation already in the early stages of building design [10], as well as offering the possibility for","cbCaidq3EIhsJgVV","https://ap.wps.com/l/cbCaidq3EIhsJgVV","pdf",10139405,1,23,"English","en",105,"# Introduction\n# BIM-enabled design and integration for rooftop PV\n## BIM simulation and performance optimization\n# Methodology and data collection\n## Performance assessment with IEC 61724 metrics\n# Machine learning energy forecasting\n## Algorithm comparison and hyperparameter tuning\n# Conclusions and implications for BAPV","[{\"question\":\"What goal does the study pursue for rooftop BAPV systems?\",\"answer\":\"It develops a strategy to maximize Building Applied Photovoltaics potential by combining operational data, BIM, and machine learning tools for energy forecasting and improved integration.\"},{\"question\":\"How does BIM contribute to the proposed optimization workflow?\",\"answer\":\"BIM simulations help identify the correct PV design process and support integration of the system into built settings, enabling accurate modeling of building characteristics.\"},{\"question\":\"Which machine learning method performs best for daily energy production forecasting?\",\"answer\":\"The random forest model is identified as the best forecasting model, and the study also reports the impact of hyperparameter tuning on all models’ efficiency.\"}]","Exploiting building information modeling and machine learning for optimizing rooftop photovoltaic systems - Research overview | PDF",1785821359,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"exploiting-building-information-modeling-and-machine-learning-for-optimizing-rooftop-photovoltaic-systems-research-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploiting-building-information-modeling-and-machine-learning-for-optimizing-rooftop-photovoltaic-systems-research-overview/124283/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What goal does the study pursue for rooftop BAPV systems?","Question",{"text":75,"@type":76},"It develops a strategy to maximize Building Applied Photovoltaics potential by combining operational data, BIM, and machine learning tools for energy forecasting and improved integration.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does BIM contribute to the proposed optimization workflow?",{"text":80,"@type":76},"BIM simulations help identify the correct PV design process and support integration of the system into built settings, enabling accurate modeling of building characteristics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning method performs best for daily energy production forecasting?",{"text":84,"@type":76},"The random forest model is identified as the best forecasting model, and the study also reports the impact of hyperparameter tuning on all models’ efficiency.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]