Performance Evaluation of Multiple Machine Learning Models in Predicting Power Generation for a Grid-Connected 300 MW Solar Farm

Obaid Aldosari (), Salem Batiyah, Murtada Elbashir, Waleed Alhosaini and Kanagaraj Nallaiyagounder
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Obaid Aldosari: Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
Salem Batiyah: Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Industrial 46452, Saudi Arabia
Murtada Elbashir: Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
Waleed Alhosaini: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Kanagaraj Nallaiyagounder: Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia

Energies, 2024, vol. 17, issue 2, 1-17

Abstract: Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms.

Keywords: machine learning; neural network; power prediction; photovoltaic; solar farm; Saudi Arabia (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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