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DC Field | Value | Language |
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dc.contributor.author | Radhi, SM | - |
dc.contributor.author | Al-Majidi, SD | - |
dc.contributor.author | Abbod, MF | - |
dc.contributor.author | Al-Raweshidy, HS | - |
dc.date.accessioned | 2024-09-03T13:05:50Z | - |
dc.date.available | 2024-09-03T13:05:50Z | - |
dc.date.issued | 2024-08-29 | - |
dc.identifier | ORCiD: Sadeq D. Al-Majidi https://orcid.org/0000-0002-3231-6830 | - |
dc.identifier | ORCiD: Maysam Abbod https://orcid.org/0000-0002-8515-7933 | - |
dc.identifier | ORCiD: Hamed Al-Raweshidy https://orcid.org/0000-0002-3702-8192 | - |
dc.identifier.citation | Radhi, S.M. et al . (2024) 'Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting', Energies, 17 (17), 4301, pp. 1 - 23. doi: 10.3390/en17174301. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29654 | - |
dc.description | Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. | en_US |
dc.description.abstract | A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively. | en_US |
dc.description.sponsorship | This research received no external funding. | en_US |
dc.format.extent | 1 - 23 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | neural network | en_US |
dc.subject | genetic algorithm | en_US |
dc.subject | gray wolf optimization | en_US |
dc.subject | photovoltaic | en_US |
dc.subject | prediction model | en_US |
dc.subject | machine learning | en_US |
dc.title | Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-08-24 | - |
dc.identifier.doi | https://doi.org/10.3390/en17174301 | - |
dc.relation.isPartOf | Energies | - |
pubs.issue | 17 | - |
pubs.publication-status | Published online | - |
pubs.volume | 17 | - |
dc.identifier.eissn | 1996-1073 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 14.9 MB | Adobe PDF | View/Open |
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