Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting

Shahad Mohammed Radhi, Sadeq D. Al-Majidi (), Maysam F. Abbod () and Hamed S. Al-Raweshidy
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Shahad Mohammed Radhi: Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq
Sadeq D. Al-Majidi: Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq
Maysam F. Abbod: Department of Electronic and Electrical Engineering, College of Engineering, Brunel University London, Uxbridge UB8 3PH, UK
Hamed S. Al-Raweshidy: Department of Electronic and Electrical Engineering, College of Engineering, Brunel University London, Uxbridge UB8 3PH, UK

Energies, 2024, vol. 17, issue 17, 1-23

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: R 2 , 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.

Keywords: neural network; genetic algorithm; gray wolf optimization; photovoltaic; prediction model; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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