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Extreme Learning Machines for Solar Photovoltaic Power Predictions

Author

Listed:
  • Sameer Al-Dahidi

    (Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan)

  • Osama Ayadi

    (Department of Mechanical Engineering, School of Engineering, The University of Jordan, Amman 11942, Jordan)

  • Jehad Adeeb

    (Renewable Energy Center, Applied Science Private University, Amman 11931, Jordan)

  • Mohammad Alrbai

    (Department of Mechanical Engineering, School of Engineering, The University of Jordan, Amman 11942, Jordan)

  • Bashar R. Qawasmeh

    (Department of Mechanical Engineering, School of Engineering, The University of Jordan, Amman 11942, Jordan)

Abstract

The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task.

Suggested Citation

  • Sameer Al-Dahidi & Osama Ayadi & Jehad Adeeb & Mohammad Alrbai & Bashar R. Qawasmeh, 2018. "Extreme Learning Machines for Solar Photovoltaic Power Predictions," Energies, MDPI, vol. 11(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2725-:d:175040
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    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Burgholzer, Bettina & Auer, Hans, 2016. "Cost/benefit analysis of transmission grid expansion to enable further integration of renewable electricity generation in Austria," Renewable Energy, Elsevier, vol. 97(C), pages 189-196.
    3. Utpal Kumar Das & Kok Soon Tey & Mehdi Seyedmahmoudian & Mohd Yamani Idna Idris & Saad Mekhilef & Ben Horan & Alex Stojcevski, 2017. "SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions," Energies, MDPI, vol. 10(7), pages 1-17, June.
    4. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    5. Al-omary, Murad & Kaltschmitt, Martin & Becker, Christian, 2018. "Electricity system in Jordan: Status & prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2398-2409.
    6. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    7. Maghami, Mohammad Reza & Hizam, Hashim & Gomes, Chandima & Radzi, Mohd Amran & Rezadad, Mohammad Ismael & Hajighorbani, Shahrooz, 2016. "Power loss due to soiling on solar panel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1307-1316.
    8. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
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    2. Gabriel de Freitas Viscondi & Solange N. Alves-Souza, 2021. "Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation," Energies, MDPI, vol. 14(18), pages 1-15, September.
    3. Muzhou Hou & Tianle Zhang & Futian Weng & Mumtaz Ali & Nadhir Al-Ansari & Zaher Mundher Yaseen, 2018. "Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 11(12), pages 1-19, December.
    4. Li, Marui & Dong, Chaoyu & Xiong, Binyu & Mu, Yunfei & Yu, Xiaodan & Xiao, Qian & Jia, Hongjie, 2022. "STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety," Applied Energy, Elsevier, vol. 328(C).
    5. Na Sun & Nan Zhang & Shuai Zhang & Tian Peng & Wei Jiang & Jie Ji & Xiangmiao Hao, 2022. "An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    6. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    7. Mario Tovar & Miguel Robles & Felipe Rashid, 2020. "PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México," Energies, MDPI, vol. 13(24), pages 1-15, December.
    8. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    9. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
    10. Evgeny Solomin & Shanmuga Priya Selvanathan & Sudhakar Kumarasamy & Anton Kovalyov & Ramyashree Maddappa Srinivasa, 2021. "The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations," Energies, MDPI, vol. 14(9), pages 1-18, May.
    11. Honghai Niu & Yu Yang & Lingchao Zeng & Yiguo Li, 2021. "ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power," Energies, MDPI, vol. 14(3), pages 1-15, January.
    12. Ju-Yeol Ryu & Bora Lee & Sungho Park & Seonghyeon Hwang & Hyemin Park & Changhyeong Lee & Dohyeon Kwon, 2022. "Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models," Energies, MDPI, vol. 15(24), pages 1-14, December.
    13. Wei Li & Hui Ren & Ping Chen & Yanyang Wang & Hailong Qi, 2020. "Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review," Energies, MDPI, vol. 13(22), pages 1-25, November.
    14. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    15. Sameer Al-Dahidi & Salah Al-Nazer & Osama Ayadi & Shuruq Shawish & Nahed Omran, 2020. "Analysis of the Effects of Cell Temperature on the Predictability of the Solar Photovoltaic Power Production," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 208-219.

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