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Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques / ALI LARI

Swansea University Author: ALI LARI

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DOI (Published version): 10.23889/SUThesis.66094

Abstract

The 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 machinelearning (ML...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Egweb, A.
URI: https://cronfa.swan.ac.uk/Record/cronfa66094
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first_indexed 2024-04-19T14:42:28Z
last_indexed 2024-04-19T14:42:28Z
id cronfa66094
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spelling v2 66094 2024-04-19 Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques 586d95b82fee51cbc2ded3e1a6996083 ALI LARI ALI LARI true false 2024-04-19 The 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 machinelearning (ML) to estimate solar energy production with the goal of revolutionizing decision-making 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 R-squared values compared to other methods.The 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. E-Thesis Swansea University, Wales, UK Renewable energy 28 2 2024 2024-02-28 10.23889/SUThesis.66094 COLLEGE NANME COLLEGE CODE Swansea University Egweb, A. Doctoral Ph.D Qatar Research Leadership Program Qatar Research Leadership Program 2024-06-20T16:12:20.9095368 2024-04-19T15:16:53.1824496 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering ALI LARI 1 66094__30713__ac1c85bc4a3241e887d626517bec6fc0.pdf 2024_Lari_A.final.66094.pdf 2024-06-20T16:11:56.3430619 Output 4162994 application/pdf E-Thesis – open access true Copyright: The Author, Ali Jassim M Lari, 2023 true eng
title Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
spellingShingle Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
ALI LARI
title_short Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
title_full Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
title_fullStr Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
title_full_unstemmed Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
title_sort Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
author_id_str_mv 586d95b82fee51cbc2ded3e1a6996083
author_id_fullname_str_mv 586d95b82fee51cbc2ded3e1a6996083_***_ALI LARI
author ALI LARI
author2 ALI LARI
format E-Thesis
publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUThesis.66094
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
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description The 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 machinelearning (ML) to estimate solar energy production with the goal of revolutionizing decision-making 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 R-squared values compared to other methods.The 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.
published_date 2024-02-28T16:12:44Z
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score 11.036006

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