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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power o...

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Main Authors: Mohd Herwan, Sulaiman, Zuriani, Mustaffa
Format: Article
Language:English
Published: Elsevier B.V. 2024
Subjects:
QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41464/1/Forecasting%20solar%20power%20generation%20using%20evolutionary%20mating.pdf
http://umpir.ump.edu.my/id/eprint/41464/
https://doi.org/10.1016/j.egyai.2024.100371
https://doi.org/10.1016/j.egyai.2024.100371
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spelling my.ump.umpir.414642024-06-05T03:48:40Z http://umpir.ump.edu.my/id/eprint/41464/ Forecasting solar power generation using evolutionary mating algorithm-deep neural networks Mohd Herwan, Sulaiman Zuriani, Mustaffa QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability. Elsevier B.V. 2024-05 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/41464/1/Forecasting%20solar%20power%20generation%20using%20evolutionary%20mating.pdf Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2024) Forecasting solar power generation using evolutionary mating algorithm-deep neural networks. Energy and AI, 16 (100371). pp. 1-17. ISSN 2666-5468. (Published) https://doi.org/10.1016/j.egyai.2024.100371 https://doi.org/10.1016/j.egyai.2024.100371
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
Mohd Herwan, Sulaiman
Zuriani, Mustaffa
Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
description This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.
format Article
author Mohd Herwan, Sulaiman
Zuriani, Mustaffa
author_facet Mohd Herwan, Sulaiman
Zuriani, Mustaffa
author_sort Mohd Herwan, Sulaiman
title Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
title_short Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
title_full Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
title_fullStr Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
title_full_unstemmed Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
title_sort forecasting solar power generation using evolutionary mating algorithm-deep neural networks
publisher Elsevier B.V.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41464/1/Forecasting%20solar%20power%20generation%20using%20evolutionary%20mating.pdf
http://umpir.ump.edu.my/id/eprint/41464/
https://doi.org/10.1016/j.egyai.2024.100371
https://doi.org/10.1016/j.egyai.2024.100371
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score 13.232414

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