sustainability Review Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend Muhammad Shahzad Nazir 1,* , Fahad Alturise 2 , Sami Alshmrany 3, Hafiz. M. J Nazir 4, Muhammad Bilal 5 , Ahmad N. Abdalla 6, P. Sanjeevikumar 7 and Ziad M. Ali 8,9 1 Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China 2 Computer Department, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Saudi Arabia; [email protected] 3 Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; [email protected] 4 Institute of Advance Space Research Technology, School of Networking, Guangzhou University, Guangzhou 510006, China; [email protected] 5 School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 6 Faculty of Information and Communication Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 7 Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark; [email protected] 8 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia; [email protected] 9 Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt * Correspondence: [email protected] or [email protected]; Tel.: +86-1322-271-7968 Received: 8 April 2020; Accepted: 23 April 2020; Published: 6 May 2020 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 1. Introduction The advancement of civilizations, and their capability to withstand bulk populations, are similar challenges to changes in the volume and nature of energy available to meet the demand for nourishment, quality of life and to complete tasks [1–3]. 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The countries with the largest wind capacity are China, the United States, the United Kingdom, Germany, Canada, Italy, France, India, and Brazil [7]. China’s installed capacity is 221 GW, and is the world leader in wind energy, producing more than a third of the world’s capacity. The province of Gansu has the largest onshore wind farm in the world, and currently has an output of 7965 MW: five times more than its closest rival. The farm currently operates at only 40% of its capacity, and a total of 120,000 MW can be installed by 2020 to achieve a total capacity of 20,000 MW (20 GW), which is expected to cost USD 17.5 billion. Despite their size, Gansu turbines appear to be “scarecrow inactive”, due to lower demand [8,9]. The USA occupies second place, with 96.4 GW installed capacity, and is particularly resistant to onshore wind energy. Six of the ten largest onshore wind farms are located in the United States. These include the Alta Wind Energy Center in California, the second-largest onshore wind farm in the world with an output of 1548 MW, Shepherd’s Flat wind farm in Oregon (845 MW), and the Roscoe wind farm in Texas (781 MW). Texas alone produces 24.9 GW of American wind power, delivering more wind power than the other 25 countries. Germany has the highest installed wind power in Europe with 59.3 GW. The