[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126159-en":3,"doc-seo-126159-105":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":11,"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},126159,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning of Solar Energy Forecasting using ensemble LSTM method","The paper addresses uncertainty in photovoltaic integration into power grids by focusing on machine-learning forecasting of solar power generation. It presents an ensemble Long Short-Term Memory (LSTM) approach to improve prediction accuracy and reduce mean square error for time series tasks such as solar irradiance forecasting. Temporal volatility is further examined using SARIMA/ARIMA models to characterize fluctuations, which can trigger frequency instability, dispatch difficulties, and grid surges in current/voltage.","This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI: 10. 1109/UPEC61344 .2024. 10892541, 2024 59th International Universities Power Engineering Conference (UPEC)  \nMachine Learning of Solar Energy Forecasting using ensemble LSTM method  \nSahar Daebes  \nDepartment of Electronic and Electrical Engineering College of Engineering, Design and Physical Sciences Brunel University London, UK  \n[Sahar.daebes@brunel.ac.uk](Sahar.daebes@brunel.ac.uk)  \nDr Mohamed Darwish Department of Electronic and Electrical Engineering College of Engineering, Design and Physical Sciences Brunel University London, UK  \n[Mohamed.darwish@brunel.ac.uk](Mohamed.darwish@brunel.ac.uk)  \nDr Chun Sing Lai  \nDepartment of Electronic and Electrical Engineering College of Engineering, Design and Physical Sciences Brunel University London, UK  \n[chunsing.lai@brunel.ac.uk](chunsing.lai@brunel.ac.uk)  \nAbstract— The transition in energy systems aims for efficiency improvements at a higher level, targeting the reduction of climate change impacts. Investing in solar energy, endorsed by the global scientific community, is essential. One of the core obstacles hindering the seamless integration of photovoltaic (PV) systems into power grids is the associated uncertainty. This paper focuses on machine learning forecasting algorithms for solar power generation, specifically using the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN) . Accurate prediction models are crucial for maximising the efficiency and reliability of solar energy systems, especially with high PV penetration. The LSTM architecture's ability to capture temporal dependencies makes it well-suited for time series forecasting tasks such as solar irradiance prediction. Using ensemble LSTM improves output accuracy and reduces mean square error in solar energy plant production. Temporal variations in solar power production, analysed using SARIMA/ARIMA models, highlight the volatility of PV power generation, which causes issues such as frequency instability, dispatch difficulties, and surges in current/voltage on the grid.  \nKeywords— Machine Learning algorithms ML, Long Short-Term Memory LSTM, transition energy system, PV forecasting algorithms, SARIMA / ARIMA  \nI. INTRODUCTION  \nHigher grid system integration of PV power is now required due to the recent exponential growth in PV installations. Managing supply and demand, saving costs, maintaining grid stability, planning, and investing, among other tasks, becomes difficult without accurate solar energy generation forecasting[1] .  \nGrid operators can effectively control the supply and demand for electricity in real-time by accurately forecasting solar energy production. This ensures efficient electricity distribution to customers and helps prevent blackouts [2] . Also, Utility companies can prevent the overproduction or underproduction of solar energy by using effective forecasts. This can reduce expenses by lowering fuel expenses (for conventional power generation) or eliminating the need for expensive energy storage systems [3] . Also, by predicting fluctuations in solar energy production, grid operators can anticipate and mitigate any likely impact on the grid's stability. Additionally, precise forecasting becomes more crucial the more solar energy is added to the electrical system.  \nIt allows grid operators to seamlessly integrate solar energy into the system and ensures energy reliability in the future [4] . When it comes to energy production, transmission, and distribution, solar energy forecasting can help energy companies with planning and investment, which is another essential component. It makes it possible to forecast revenue streams more precisely, which is beneficial for financial planning and investment decisions [5] .  \nII. PREDICTING SOLAR POWER PRODUCTION USING MACHINE  \nLEARNING ","cbCaikxVxGhtgDlc","https://ap.wps.com/l/cbCaikxVxGhtgDlc","pdf",805297,1,6,"English","en",105,"# Introduction\n# Predicting Solar Power Production Using Machine Learning Algorithms","[{\"question\":\"Why is accurate solar energy forecasting important for PV grid integration?\",\"answer\":\"Accurate forecasting helps manage supply and demand, supports grid stability, improves dispatch decisions, and enables planning and investment with reduced operational risk.\"},{\"question\":\"What forecasting method does the paper primarily focus on?\",\"answer\":\"The paper focuses on machine learning forecasting using an ensemble Long Short-Term Memory (LSTM) approach for solar power and irradiance time-series prediction.\"},{\"question\":\"How do ensemble LSTM and statistical models contribute to the study?\",\"answer\":\"Ensemble LSTM improves output accuracy and lowers mean square error, while SARIMA/ARIMA models help analyze temporal variations and quantify PV generation volatility affecting the grid.\"}]","Machine Learning of Solar Energy Forecasting using ensemble LSTM method | 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is accurate solar energy forecasting important for PV grid integration?","Question",{"text":76,"@type":77},"Accurate forecasting helps manage supply and demand, supports grid stability, improves dispatch decisions, and enables planning and investment with reduced operational risk.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What forecasting method does the paper primarily focus on?",{"text":81,"@type":77},"The paper focuses on machine learning forecasting using an ensemble Long Short-Term Memory (LSTM) approach for solar power and irradiance time-series prediction.",{"name":83,"@type":74,"acceptedAnswer":84},"How do ensemble LSTM and statistical models contribute to the study?",{"text":85,"@type":77},"Ensemble LSTM improves output accuracy and lowers mean square error, while SARIMA/ARIMA models help analyze temporal variations and quantify PV generation volatility affecting the 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