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

Muhammad Shahzad Nazir (), Fahad Alturise, Sami Alshmrany, Hafiz. M. J Nazir, Muhammad Bilal, Ahmad N. Abdalla, P. Sanjeevikumar and Ziad M. Ali
Additional contact information
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

Sustainability, 2020, vol. 12, issue 9, 1-27

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.

Keywords: environment; sustainability; quality of life; forecasting; instrument; ANN; wind energy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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