IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6759-d658274.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6759/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/20/6759/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Bilal Awan & Muhammad Zubair & Praveen R. P. & Ahmed G. Abokhalil, 2018. "Solar Energy Resource Analysis and Evaluation of Photovoltaic System Performance in Various Regions of Saudi Arabia," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    2. Tayeb Brahimi, 2019. "Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia," Energies, MDPI, vol. 12(24), pages 1-16, December.
    3. Saber, Esmail M. & Lee, Siew Eang & Manthapuri, Sumanth & Yi, Wang & Deb, Chirag, 2014. "PV (photovoltaics) performance evaluation and simulation-based energy yield prediction for tropical buildings," Energy, Elsevier, vol. 71(C), pages 588-595.
    4. Christil Pasion & Torrey Wagner & Clay Koschnick & Steven Schuldt & Jada Williams & Kevin Hallinan, 2020. "Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data," Energies, MDPI, vol. 13(10), pages 1-14, May.
    5. Richard G. Newell & Yifei Qian & Daniel Raimi, 2016. "Global Energy Outlook 2015," NBER Working Papers 22075, National Bureau of Economic Research, Inc.
    6. Chih-Chiang Wei, 2019. "Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings," Energies, MDPI, vol. 12(18), pages 1-18, September.
    7. Majid Almaraashi, 2017. "Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    8. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & Andres Muñoz-Jimenez & Pedro M. Lara-Santillan, 2013. "Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    2. Awan, Ahmed Bilal & Zubair, Muhammad & Chandra Mouli, Kotturu V.V., 2020. "Design, optimization and performance comparison of solar tower and photovoltaic power plants," Energy, Elsevier, vol. 199(C).
    3. Kosorić, Vesna & Huang, Huajing & Tablada, Abel & Lau, Siu-Kit & Tan, Hugh T.W., 2019. "Survey on the social acceptance of the productive façade concept integrating photovoltaic and farming systems in high-rise public housing blocks in Singapore," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 197-214.
    4. Ren, Haoshan & Ma, Zhenjun & Chan, Antoni B. & Sun, Yongjun, 2023. "Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities," Energy, Elsevier, vol. 263(PA).
    5. Yitian Xing & Fue-Sang Lien & William Melek & Eugene Yee, 2022. "A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model," Energies, MDPI, vol. 15(15), pages 1-35, July.
    6. Hui Zhang & Xiaoxi Huang & Zhengwei Wang & Shiyu Jin & Benlin Xiao & Yanyan Huang & Wei Zhong & Aofei Meng, 2024. "An Estimation of the Available Spatial Intensity of Solar Energy in Urban Blocks in Wuhan, China," Energies, MDPI, vol. 17(5), pages 1-26, February.
    7. Aleksander Radovan & Viktor Šunde & Danijel Kučak & Željko Ban, 2021. "Solar Irradiance Forecast Based on Cloud Movement Prediction," Energies, MDPI, vol. 14(13), pages 1-25, June.
    8. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    9. Amjad Ali, 2023. "Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    10. Biying Yu & Guangpu Zhao & Runying An, 2019. "Framing the picture of energy consumption in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1469-1490, December.
    11. Khadijah Barashid & Amr Munshi & Ahmad Alhindi, 2023. "Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia," Energies, MDPI, vol. 16(2), pages 1-22, January.
    12. Tuan-Viet Hoang & Pouya Ifaei & Kijeon Nam & Jouan Rashidi & Soonho Hwangbo & Jong-Min Oh & ChangKyoo Yoo, 2018. "Optimal Management of a Hybrid Renewable Energy System Coupled with a Membrane Bioreactor Using Enviro-Economic and Power Pinch Analyses for Sustainable Climate Change Adaption," Sustainability, MDPI, vol. 11(1), pages 1-22, December.
    13. Yahya Z. Alharthi, 2023. "Performance Analysis Using Multi-Year Parameters for a Grid-Connected Wind Power System," Energies, MDPI, vol. 16(5), pages 1-20, February.
    14. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    15. Saheli Biswas & Shambhu Singh Rathore & Aniruddha Pramod Kulkarni & Sarbjit Giddey & Sankar Bhattacharya, 2021. "A Theoretical Study on Reversible Solid Oxide Cells as Key Enablers of Cyclic Conversion between Electrical Energy and Fuel," Energies, MDPI, vol. 14(15), pages 1-18, July.
    16. Jizhong Shao & Huixian Chen & Ting Zhu, 2016. "Solar Energy Block-Based Residential Construction for Rural Areas in the West of China," Sustainability, MDPI, vol. 8(4), pages 1-21, April.
    17. Peng, Jinqing & Curcija, Dragan C. & Thanachareonkit, Anothai & Lee, Eleanor S. & Goudey, Howdy & Selkowitz, Stephen E., 2019. "Study on the overall energy performance of a novel c-Si based semitransparent solar photovoltaic window," Applied Energy, Elsevier, vol. 242(C), pages 854-872.
    18. Coilín ÓhAiseadha & Gerré Quinn & Ronan Connolly & Michael Connolly & Willie Soon, 2020. "Energy and Climate Policy—An Evaluation of Global Climate Change Expenditure 2011–2018," Energies, MDPI, vol. 13(18), pages 1-49, September.
    19. Yuhao Zhang & Ting Li & Tianyi Ma & Dongsheng Yang & Xiaolong Sun, 2024. "Short-Term Photovoltaic Power Prediction Based on Extreme Learning Machine with Improved Dung Beetle Optimization Algorithm," Energies, MDPI, vol. 17(4), pages 1-24, February.
    20. Deepak Jain Veerendra Kumar & Lelia Deville & Kenneth A. Ritter & Johnathan Richard Raush & Farzad Ferdowsi & Raju Gottumukkala & Terrence Lynn Chambers, 2022. "Performance Evaluation of 1.1 MW Grid-Connected Solar Photovoltaic Power Plant in Louisiana," Energies, MDPI, vol. 15(9), pages 1-21, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6759-:d:658274. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.