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

Author

Listed:
  • 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
    Laboratoire des Systèmes Electriques, Ecole Nationale d’Ingénieurs de Tunis, Université de Tunis El Manar, Tunis 1002, Tunisia)

  • Salem Alelyani

    (Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
    College of Computer Science, 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)

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.

Suggested Citation

  • Mohamed Mohana & Abdelaziz Salah Saidi & Salem Alelyani & Mohammed J. Alshayeb & Suhail Basha & Ali Eisa Anqi, 2021. "Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning," Energies, MDPI, vol. 14(20), pages 1-18, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6759-:d:658274
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    References listed on IDEAS

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    Cited by:

    1. Sultan J. Alharbi & Abdulaziz S. Alaboodi, 2023. "A Review on Techno-Economic Study for Supporting Building with PV-Grid-Connected Systems under Saudi Regulations," Energies, MDPI, vol. 16(3), pages 1-14, February.
    2. Guici Chen & Tingting Zhang & Wenyu Qu & Wenbo Wang, 2023. "Photovoltaic Power Prediction Based on VMD-BRNN-TSP," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    3. Lioua Kolsi & Sameer Al-Dahidi & Souad Kamel & Walid Aich & Sahbi Boubaker & Nidhal Ben Khedher, 2022. "Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    4. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    5. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.
    6. Wioletta Wierzbicka, 2022. "Activities Undertaken in the Member Cities of the Polish National Cittaslow Network in the Area of “Energy and Environmental Policy”," Energies, MDPI, vol. 15(4), pages 1-16, February.
    7. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.

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