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Title: | Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting |
Authors: | Radhi, SM Al-Majidi, SD Abbod, MF Al-Raweshidy, HS |
Keywords: | neural network;genetic algorithm;gray wolf optimization;photovoltaic;prediction model;machine learning |
Issue Date: | 29-Aug-2024 |
Publisher: | MDPI |
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. |
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. |
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. |
URI: | https://bura.brunel.ac.uk/handle/2438/29654 |
DOI: | https://doi.org/10.3390/en17174301 |
Other Identifiers: | ORCiD: Sadeq D. Al-Majidi https://orcid.org/0000-0002-3231-6830 ORCiD: Maysam Abbod https://orcid.org/0000-0002-8515-7933 ORCiD: Hamed Al-Raweshidy https://orcid.org/0000-0002-3702-8192 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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