[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125005-en":3,"doc-seo-125005-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},125005,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning","This paper investigates short-term forecasting of rooftop retrofitted photovoltaic (PV) power generation using a Neural Networks (NN) model, emphasizing its value for energy management and grid integration. The research leverages 15-minute interval data from a 570.4 kWp rooftop PV system at Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) from January 29 to February 4, 2024. Thirty-three features (e.g., ambient temperature, horizontal irradiation, AC voltages, AC currents, and MPPTs) predict total active power. An NN model is benchmarked against LSTM, GRU, RF, and kNN.","Journal of Building Engineering 94 (2024) 109948  \nContents lists available at ScienceDirect  \nJournal of Building Engineering  \njournal [homepage:](homepage: www.elsevier.com/locate/jobe)[ www.elsevier.com/locate/jobe](homepage: www.elsevier.com/locate/jobe)  \n| Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Mohd Herwan Sulaiman a, * , Mohd Shawal Jadina , Zuriani Mustaffab , Hamdan Daniyala , Mohd Nurulakla Mohd Azlanc\u003Cbr>a Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600, Pekan, Pahang, Malaysia\u003Cbr>b Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600, Pekan, Pahang, Malaysia\u003Cbr>c Electrical and Energy Efficiency Section, Centre for Property Management and Development, Universiti Malaysia Pahang AL-Sultan Abdullah, Pekan, 26600, Malaysia |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Rooftop retrofitted photovoltaic (PV) Machine learning\u003Cbr>Short-term forecasting\u003Cbr>Photovoltaic power generation Solar energy |  | This paper explores short-term forecasting of rooftop retrofitted photovoltaic (PV) power generation using a Neural Networks (NN) model, highlighting its importance for energy management and grid integration. The study used data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) of the Faculty of Electrical & Electronics Engineering Technology (FTKEE), capturing a 570.4 kWp rooftop PV system. The data, collected at 15-min intervals from January 29 to February 4, 2024, included thirty-three input features such as ambient temperature, horizontal irradiation, AC voltages, AC currents, and Maximum Power Point Trackers (MPPTs). The target was the total active power in kilowatts. The methodology involved partitioning the data into a training set covering the first five days and a testing set for the last two days. The NN model was compared with other machine learning approaches, including Long Short-Term Memory Networks (LSTM), Gated Recurrent Units (GRU), Random Forest (RF), and k-Nearest Neighbors (kNN). Performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum Error, and Standard Deviation were used to evaluate the models. The results showed that the NN model outperformed all other models, achieving an RMSE of 0.7098, an MAE of 0.3629, a Maximum Error of 3.901, and a Standard Deviation of 0.7076. These findings suggest that NN effectively capture complex patterns in rooftop PV system data, contributing to enhanced reliability and efficiency in short-term solar power forecasting. The study’s implications extend to improved grid management and energy efficiency, underlining the significance of advanced machine learning techniques in renewable energy forecasting. |  |\n\n1. Introduction  \nThe growing integration of renewable energy sources, particularly solar photovoltaic (PV) systems, plays a pivotal role in the global transition towards sustainable energy practices. Recognizing the urgency, the International Energy Agency reports a surge in the demand for clean energy, driven by heightened environmental concerns and the imperative to mitigate climate change [1]. Solar technologies, including rooftop retrofitted PV systems, have emerged as crucial contributors to this transformative journey, ushering in  \n* Corresponding author.  \nE-mail [address:](address: herwan@umpsa.edu.my)[ herwan@umpsa.edu.my](address: herwan@umpsa.edu.my) (M.H. Sulaiman).  \n[https://doi.org/10.1016/j.jobe.2024.109948](https://doi.org/10.1016/j.jobe.2024.109948)  \nReceived 7 March 2024; Received in revised form 22 May 2024; Accepted 13 June 2024 Available online 14 June 2024  \n2352-7102/© 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","cbCaiewZeHmYYozu","https://ap.wps.com/l/cbCaiewZeHmYYozu","pdf",259820,1,"English","en",105,"# Introduction\n## Data and Feature Inputs\n## Modeling Methodology and Comparisons\n## Evaluation Metrics and Results\n## Implications for Grid Management","[{\"question\":\"What forecasting task does the study address?\",\"answer\":\"The study focuses on short-term forecasting of total active power for rooftop retrofitted photovoltaic (PV) power generation.\"},{\"question\":\"What data and time resolution are used?\",\"answer\":\"It uses 15-minute interval measurements collected from January 29 to February 4, 2024, from a 570.4 kWp rooftop PV system.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"The Neural Networks (NN) model outperformed LSTM, GRU, Random Forest (RF), and kNN, evaluated using RMSE, MAE, Maximum Error, and Standard Deviation.\"}]","Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning | PDF",1785896055,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"short-term-forecasting-of-rooftop-retrofitted-photovoltaic-power-generation-using-machine-learning","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/short-term-forecasting-of-rooftop-retrofitted-photovoltaic-power-generation-using-machine-learning/125005/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What forecasting task does the study address?","Question",{"text":73,"@type":74},"The study focuses on short-term forecasting of total active power for rooftop retrofitted photovoltaic (PV) power generation.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What data and time resolution are used?",{"text":78,"@type":74},"It uses 15-minute interval measurements collected from January 29 to February 4, 2024, from a 570.4 kWp rooftop PV system.",{"name":80,"@type":71,"acceptedAnswer":81},"Which model performed best and how was it evaluated?",{"text":82,"@type":74},"The Neural Networks (NN) model outperformed LSTM, GRU, Random Forest (RF), and kNN, evaluated using RMSE, MAE, Maximum Error, and Standard Deviation.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]