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Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

Author

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  • Muhammad Shahzad Nazir

    (Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Fahad Alturise

    (Computer Department, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Saudi Arabia)

  • Sami Alshmrany

    (Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia)

  • Hafiz. M. J Nazir

    (Institute of Advance Space Research Technology, School of Networking, Guangzhou University, Guangzhou 510006, China)

  • Muhammad Bilal

    (School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Ahmad N. Abdalla

    (Faculty of Information and Communication Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • P. Sanjeevikumar

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Ziad M. Ali

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

Suggested Citation

  • Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3778-:d:354646
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    3. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    4. Ola G. El‐Taliawi & Nihit Goyal & Michael Howlett, 2021. "Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching," Review of Policy Research, Policy Studies Organization, vol. 38(6), pages 640-660, November.
    5. Fotopoulou, Maria & Rakopoulos, Dimitrios & Petridis, Stefanos & Drosatos, Panagiotis, 2024. "Assessment of smart grid operation under emergency situations," Energy, Elsevier, vol. 287(C).
    6. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    7. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    8. Christy Pérez-Albornoz & Ángel Hernández-Gómez & Victor Ramirez & Damien Guilbert, 2023. "Forecast Optimization of Wind Speed in the North Coast of the Yucatan Peninsula, Using the Single and Double Exponential Method," Clean Technol., MDPI, vol. 5(2), pages 1-22, June.
    9. Paxis Marques João Roque & Shyama Pada Chowdhury & Zhongjie Huan, 2021. "Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study," Energies, MDPI, vol. 14(14), pages 1-22, July.
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