[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118145-en":3,"doc-seo-118145-105":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},118145,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning Integration for Enhanced Solar Power Generation Forecasting","This paper reviews advances in machine learning techniques for improved photovoltaic power generation forecasting. Solar power is inherently intermittent because weather conditions strongly affect output, so accurate PV power generation forecasting is essential for stable and reliable power-system operation. The review emphasizes deep learning approaches, especially an LSTM-based framework, and presents physics-constrained LSTM (PCLSTM) to mitigate limits of conventional models that require large datasets. Results indicate improved performance under sparse-data conditions, and a related study from Morocco highlights ANN as the most effective option for practical use.","Machine Learning Integration for Enhanced Solar Power Generation Forecasting  \nDr D David Winster Praveenraj 1, Madeswaran. A2, Rishab Pastariya3, Deepti Sharma4 , Kassem Abootharmahmoodshakir5, and Anishkumar Dhablia6  \n* Assistant Professor, School of Business and Management , CHRIST (Deemed to be University ) Bangalore Yeshwantpur Campus  \n† Associate Professor, Scool of Business and Management, Christ university, Yeshwanthpur Campus, Bengaluru.  \n‡ Department of Computer Science & Engineering, IES College of Technology,IES University, Bhopal, Bhopal, Madhya Pradesh 462044 India.  \n§Department of Management  \nUttaranchal Institute of Management, Uttaranchal University, Dehradun-248007, India.  \n**The Islamic university, Najaf, Iraq.  \n6Engineering Manager, AltimetrikIndia Pvt Ltd, Pune, Maharashtra, India [anishdhablia@gmail.com](anishdhablia@gmail.com).  \nAbstract.This paper reviews the advancements in machine learning techniques for enhanced solar power generation forecasting. Solar energy, a potent alternative to traditional energy sources, is inherently intermittent due to its weather-dependent nature. Accurate forecasting of photovoltaic power generation (PVPG) is paramount for the stability and reliability of power systems. The review delves into a deep learning framework that leverages the long short-term memory (LSTM) network for precise PVPG forecasting. A novel approach, the physics-constrained LSTM (PCLSTM), is introduced, addressing the limitations of conventional machine learning algorithms that rely heavily on vast data. The PC-LSTM model showcases superior forecasting capabilities, especially with sparse data, outperforming standard LSTM and other traditional methods. Furthermore, the paper examines a comprehensive study from Morocco, comparing six machine learning algorithms for solar energy production forecasting. The study underscores the Artificial Neural Network (ANN) as the most effective predictive model, offering optimal parameters for real-world applications. Such advancements not only bolster the accuracy of solar  \n*Corresponding Authour :david.winster@christuniversity.in  \n†[madeswaran.a@christ university.in](madeswaran.a@christ university.in)  \n‡[research@iesbpl.ac.in](research@iesbpl.ac.in)  \n§[deepti.sharma.sama@gmail.com](deepti.sharma.sama@gmail.com)  \n** [abathermahmood560@gmail.com](abathermahmood560@gmail.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nenergy forecasting but also pave the way for sustainable energy solutions, emphasizing the integration of these findings in practical applications like  \npredictive maintenance of PV power plants.  \n1 Introduction  \nThe global economy's growth trajectory has led to an escalating demand for electricity, with a significant portion of this demand historically met through the consumption of fossil fuels. This reliance on non-renewable energy sources has precipitated a host of environmental challenges, most notably the emission of greenhouse gases, which are primary contributors to global warming and subsequent climate change. As acountermeasure, the past decade has witnessed a surge in the generation of electricity from renewable sources, with photovoltaic (PV) energy emerging as a frontrunner. The potential of PV energy is underscored by the sheer magnitude of solar radiation the Earth receives, which is ample to cater to global human activity.  \nHowever, the efficacy of PV energy hinges on accurate forecasting of PV power generation (PVPG) . Such forecasting is pivotal for power system operations, aiding power suppliers in formulating commercial offers and ensuring the stability of power systems. The challenge lies in the inherent variability of PV outputs, which are influenced by solar irradiance and a myriad of meteorological factors. This","cbCaifUzgLFRWyyK","https://ap.wps.com/l/cbCaifUzgLFRWyyK","pdf",1546430,1,7,"English","en",105,"# Introduction\n## Solar energy growth and forecasting need\n## Forecasting approaches and role of machine learning\n# Review and discussion\n## LSTM-based deep learning for PV forecasting\n## Physics-constrained LSTM (PCLSTM)\n## Comparative study of algorithms for solar forecasting","[{\"question\":\"Why is photovoltaic power generation forecasting important?\",\"answer\":\"Accurate forecasting supports power-system operation by helping suppliers plan commercial offers and maintain stability. It is also needed because PV output varies with solar irradiance and meteorological conditions.\"},{\"question\":\"How does physics-constrained LSTM improve PV forecasting?\",\"answer\":\"The physics-constrained LSTM (PCLSTM) addresses limitations of conventional machine learning methods that depend heavily on large datasets. It enhances forecasting performance, especially when data are sparse.\"},{\"question\":\"Which algorithm was found most effective in the Morocco study?\",\"answer\":\"The study comparing six machine learning algorithms concludes that the Artificial Neural Network (ANN) provides the most effective predictive performance, with strong parameter suitability for real-world applications.\"}]","Machine Learning Integration for Enhanced Solar Power Generation Forecasting | PDF",1785681871,18,{"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},"machine-learning-integration-for-enhanced-solar-power-generation-forecasting","",{"@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/machine-learning-integration-for-enhanced-solar-power-generation-forecasting/118145/",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","2026-08-02",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},"Why is photovoltaic power generation forecasting important?","Question",{"text":75,"@type":76},"Accurate forecasting supports power-system operation by helping suppliers plan commercial offers and maintain stability. It is also needed because PV output varies with solar irradiance and meteorological conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does physics-constrained LSTM improve PV forecasting?",{"text":80,"@type":76},"The physics-constrained LSTM (PCLSTM) addresses limitations of conventional machine learning methods that depend heavily on large datasets. It enhances forecasting performance, especially when data are sparse.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm was found most effective in the Morocco study?",{"text":84,"@type":76},"The study comparing six machine learning algorithms concludes that the Artificial Neural Network (ANN) provides the most effective predictive performance, with strong parameter suitability for real-world applications.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]