[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123776-en":3,"doc-seo-123776-105":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},123776,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques","The growing demand for renewable energy sources, especially wind and solar power, increases the need for accurate production forecasts. This thesis applies machine learning (ML) to estimate solar energy generation and support better decision-making through analysis of large datasets and generation of reliable forecasts. Solar meteorological data is modeled using regression, time series analysis, and deep learning algorithms. Results show strong prediction accuracy through quantitative evaluation. Multiple Linear Regression provides reasonable predictive ability with moderate MAE and RMSE values, though with slightly lower R-squared scores than some alternatives. The work also discusses the significance of results, methodological dependability, generalizability, limitations, and recommendations for further research, highlighting broader energy-sector applications and a more sustainable future.","Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques  \nAli Jassim M Lari  \nA thesis submitted to SWANSEA UNIVERSITY in fulfilment of the requirements for the Degree of DOCTOR OF PHILOSOPHY  \nDepartment of Electronic and Electrical Engineering SWANSEA UNIVERSITY  \nJune 2023  \nCopyright: The Author, Ali Jassim M Lari, 2023  \ni  \nDistributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0) .  \nDECLARATION  \nThis work has not previously been accepted in substance for any degree and is not being concurrently submitted in candidature for any degree.  \nSigned ...........ALI JASSIM M H LARI.....................................  \nDate ...............20/06/2023..........................................................  \nSTATEMENT 1  \nThis thesis is the result of my own investigations, except where otherwise stated. Where correction services have been used, the extent and nature of the correction are clearly marked in a footnote(s) .  \nOther sources are acknowledged by footnotes giving explicit references. A bibliography is appended.  \nSigned ...........ALI JASSIM M H LARI.....................................  \nDate ...............20/06/2023..........................................................  \nSTATEMENT 2  \nI hereby give consent for my thesis, if accepted, to be available for photocopying and for interlibrary loan and for the title and summary to be made available to outside organizations.  \nSigned ...........ALI JASSIM M H LARI.................... ... ..............  \nDate   20/06/2023    \nABSTRACT  \nThe growing demand for renewable energy sources, especially wind and solar power, has increased the requirement for precise forecasts in the energy production process. Using machine learning (ML) techniques offers a revolutionary way to deal with this problem, and this thesis uses machine learning (ML) to estimate solar energy production with the goal of revolutionizing decisionmaking processes through the analysis of large datasets and the generation of accurate forecasts. Solar meteorological data is analyzed methodologically using regression, time series analysis, and deep learning algorithms. The study demonstrates how well machine learning-based forecasting works to anticipate future solar energy output. Quantitative evaluations show excellent prediction accuracy and verify the techniques used. For example, the key observations made were that the Multiple Linear Regression methods demonstrates reasonable predictive ability with moderate Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values yet slightly lower Rsquared values compared to other methods.  \nThe study also provides a reflective analysis of result significance, methodology dependability, and result generalizability, as well as a summary of its limits and recommendations for further study. The conclusion provides implications for broader applications across energy sectors and emphasizes the critical role that ML-based forecasting plays in predicting solar energy generation. By utilizing renewable energy sources like solar power, this approach aims to lessen dependency on non-renewable resources and pave the way for a more sustainable future.  \nDEDICATION  \nI want to sincerely convey my heartfelt gratitude to the State of Qatar and HH Sheikh Tamim bin Hamad Al Thani for their unwavering support and for granting me this invaluable opportunity. My profound thanks also extend to the Qatar Foundation, my sponsor, for their unwavering support and constant encouragement throughout my PhD journey. Additionally, I wish to recognize the exceptional community at Swansea University, whose pivotal role was instrumental in helping me successfully complete this remarkable journey.  \nTo my parents, I express my profound gratitude for instilling in me a passion for learning and making significant sacrifices to provide me with the best education possible. Your belief in meand endless encouragement have been a guidi","cbCaicPFO6udxlHV","https://ap.wps.com/l/cbCaicPFO6udxlHV","pdf",4321393,1,176,"English","en",105,"# Abstract\n## Renewable energy forecasting problem\n## Data and modeling methods\n## Evaluation results and key findings\n## Discussion, limitations, and recommendations","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses the need for precise forecasting of solar energy production to support decision-making in renewable energy generation.\"},{\"question\":\"Which machine learning approaches are used for solar data modeling?\",\"answer\":\"The study uses regression methods, time series analysis, and deep learning algorithms to estimate solar energy generation.\"},{\"question\":\"What do the results show about prediction accuracy?\",\"answer\":\"Quantitative evaluations demonstrate excellent prediction accuracy, and Multiple Linear Regression shows reasonable performance with moderate MAE and RMSE, though with slightly lower R-squared values than some other methods.\"}]","Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques | 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