Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning

Mohamed Mohana, Abdelaziz Salah Saidi, Salem Alelyani, Mohammed J. Alshayeb, Suhail Basha and Ali Eisa Anqi
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Mohamed Mohana: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
Abdelaziz Salah Saidi: Department of Electrical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia
Salem Alelyani: Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
Mohammed J. Alshayeb: Department of Architecture and Planning, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia
Suhail Basha: Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
Ali Eisa Anqi: Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia

Energies, 2021, vol. 14, issue 20, 1-18

Abstract: Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.

Keywords: solar photovoltaic; power prediction; residential load; environmental parameters; machine learning models; ensemble models; artificial neural networks; correlation; backward feature elimination (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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