[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127794-en":3,"doc-seo-127794-105":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},127794,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting","Photovoltaic (PV) power forecasting is essential for stabilizing hybrid power grids because PV output depends strongly on weather conditions. This work develops a short-term PV forecasting model using actual operational data from an experimental PV prototype. The dataset is grouped into sunny, cloudy, and rainy conditions across seasons, measured at 3-minute intervals to capture rapid generation changes. An ANN optimized with GA and gray wolf optimization is evaluated using R2, MAE, RMSE, and MSE.","energies   \nArticle  \nMachine Learning Approaches for Short-Term Photovoltaic Power Forecasting  \nShahad Mohammed Radhi 1, Sadeq D. Al-Majidi 1, *, Maysam F. Abbod 2, * and Hamed S. Al-Raweshidy 2  \nCitation: Radhi, S.M.; Al-Majidi, S.D.; Abbod, M.F.; Al-Raweshidy, H.S. Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting. Energies 2024, 17, 4301 . [https://doi.org/10.3390/en17174301](https://doi.org/10.3390/en17174301)  \n[Academic Editors: Jes](Academic Editors: Jes)ús Polo and Gabriel López Rodríguez  \nReceived: 11 July 2024  \nRevised: 23 August 2024  \nAccepted: 24 August 2024  \nPublished: 28 August 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq; [enghre.2202@uomisan.edu.iq](enghre.2202@uomisan.edu.iq)  \n2 Department of Electronic and Electrical Engineering, College of Engineering, Brunel University London, Uxbridge UB8 3PH, UK; [hamed.al-raweshidy@brunel.ac.uk](hamed.al-raweshidy@brunel.ac.uk)  \n* Correspondence: [sadeqalmajidi@uomisan.edu.iq](sadeqalmajidi@uomisan.edu.iq) (S.D.A.-M.); [maysam.abbod@brunel.ac.uk](maysam.abbod@brunel.ac.uk) (M.F.A.)  \nAbstract: A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraqis designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.  \nKeywords: neural network; genetic algorithm; gray wolf optimization; photovoltaic; prediction model; machine learning  \n1. Introduction  \nIn recent years, several intriguing forms of renewable energy, such as PV, wind, and geothermal, have been utilized with the power grid to provide green energy and compensate for the load demand [1,2] . However, solar PV energy is characterized by the highest development ratio in the world due to its lowest operation cost. Despite this, to address discrepancies between energy demand and production, specifically for large-scale PV systems, there is still a need for accurate forecast of PV power generation when it is connected to the power grid [3–5] . The predictive capability will reduce the impact of PV fluctuations on the power grid by enhancing stability, guaranteeing energy quality, and facilitating efficient management [6] .  \nMany scientific studies have","cbCailDlUsdrA32c","https://ap.wps.com/l/cbCailDlUsdrA32c","pdf",15253566,1,23,"English","en",105,"# Introduction\n## Methodology and data preparation\n## ANN optimization and evaluation\n## Results and comparison","[{\"question\":\"Why is short-term PV power forecasting important in hybrid power grids?\",\"answer\":\"PV output varies with weather conditions, so accurate short-term forecasts help reduce fluctuations and improve grid stability, energy quality, and operational management.\"},{\"question\":\"How was the dataset prepared for the forecasting model?\",\"answer\":\"Operational data from an experimental PV prototype were collected and classified into three atmospheric groups: sunny, cloudy, and rainy cases, with measurements taken every 3 minutes.\"},{\"question\":\"What optimization and evaluation methods were used for the ANN model?\",\"answer\":\"The study uses ANN learning enhanced by gray wolf optimization (GWO) and a genetic algorithm (GA), and evaluates performance with R2, MAE, RMSE, and MSE, comparing results across climatic conditions.\"}]","Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting | 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is short-term PV power forecasting important in hybrid power grids?","Question",{"text":76,"@type":77},"PV output varies with weather conditions, so accurate short-term forecasts help reduce fluctuations and improve grid stability, energy quality, and operational management.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the dataset prepared for the forecasting model?",{"text":81,"@type":77},"Operational data from an experimental PV prototype were collected and classified into three atmospheric groups: sunny, cloudy, and rainy cases, with measurements taken every 3 minutes.",{"name":83,"@type":74,"acceptedAnswer":84},"What optimization and evaluation methods were used for the ANN model?",{"text":85,"@type":77},"The study uses ANN learning enhanced by gray wolf optimization (GWO) and a genetic algorithm (GA), and evaluates performance with R2, MAE, RMSE, and MSE, comparing results across climatic 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