[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119252-en":3,"doc-seo-119252-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119252,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Short-Term forecasting of floating photovoltaic power generation using machine learning models","Floating photovoltaic (FPV) power generation depends on accurate short-term forecasting to improve operational efficiency and strengthen grid integration. The study applies multiple machine learning models to predict FPV power output using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) installation (157.20 kWp). Measurements collected every 15 minutes from January 15–21, 2024 include nine input variables such as ambient temperature and irradiation, with total active power as the target. Training uses the first five days; testing uses the remaining two, and Neural Networks delivers the best predictive accuracy among the evaluated models.","Cleaner Energy Systems 9 (2024) 100137  \nContents lists available at ScienceDirect  \nCleaner Energy Systems  \njournal [homepage: www.elsevier.com/locate/cles](homepage: www.elsevier.com/locate/cles)  \n| Short-Term forecasting of floating photovoltaic power generation using machine learning models\u003Cbr>Mohd Herwan Sulaiman a, * , Mohd Shawal Jadina , Zuriani Mustaffab, Mohd Nurulakla Mohd Azlanc , Hamdan Daniyala\u003Cbr>a Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600 Pekan Pahang Malaysia 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| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Floating Photovoltaic (FPV) Machine learning\u003Cbr>Short-term forecasting |  | Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations. |\n\n1. Introduction  \nThe global energy landscape has witnessed significant transformations in recent years, with an increasing focus on sustainable and renewable sources. According to the International Energy Agency (Anon., IEA, 2021), the demand for clean energy has surged, driven by environmental concerns and the need to mitigate climate change. Renewable energy (RE) technologies, including solar, have emerged as pivotal contributors to the transition towards a greener and more sustainable energy sector (Choi et al., 2023; Khare et al., 2023; Maka and Alabid, 2022; Xiong et al., 2023). Solar energy, in particular, plays a crucial role in harnessing abundant and clean power from the sun. The application of solar technologies has transcended conventional photovoltaic (PV) installations, extending to inventive solutions such as floating photovoltaic (FPV) systems (C.J et al., 2024; El Hammoumi et al., 2021; Mu˜noz-Cer´on et al., 2023; Nisar et al., 2022; Rosa-Clot, 2020).  \nThis diversification is further propelled by the increasing demand for  \nalternative solar installations, notably driven by land scarcity concerns. Notably, traditional large-scale solar farms face challenges associated with extensive land requirements. To address this limitation, floating solar farms, strategically deployed on available water bodies, have gained traction, particularly in regions grappling with land scarcity (Bi and Law, 2023; Dai et al., 2020; Song et al., 2024). This trend signifies a rapid and transformative shift towards harnessing solar energy through innovative FPV systems, providing sustainable energy sol","cbCaiuVPrxbpcCAS","https://ap.wps.com/l/cbCaiuVPrxbpcCAS","pdf",10367377,1,15,"English","en",105,"# Introduction\n## Floating photovoltaic systems and their advantages\n## Importance of short-term forecasting\n## Machine learning for FPV power prediction","[{\"question\":\"What dataset and sampling method were used for the FPV forecasting study?\",\"answer\":\"Data were collected from the UMPSA solar installation at 15-minute intervals from January 15 to January 21, 2024, using a 157.20 kWp FPV system.\"},{\"question\":\"Which machine learning models were evaluated for predicting FPV power generation?\",\"answer\":\"Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) were used to predict FPV output.\"},{\"question\":\"How were the training and testing sets constructed?\",\"answer\":\"The dataset was divided into a training set using the first five days and a testing set using the remaining two days.\"}]","Short-Term forecasting of floating photovoltaic power generation using machine learning models | 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dataset and sampling method were used for the FPV forecasting study?","Question",{"text":76,"@type":77},"Data were collected from the UMPSA solar installation at 15-minute intervals from January 15 to January 21, 2024, using a 157.20 kWp FPV system.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models were evaluated for predicting FPV power generation?",{"text":81,"@type":77},"Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) were used to predict FPV output.",{"name":83,"@type":74,"acceptedAnswer":84},"How were the training and testing sets constructed?",{"text":85,"@type":77},"The dataset was divided into a training set using the first five days and a testing set using the remaining two 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