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Researchers from Omar Halisdemir University Report Details of New Studies and Findings in the Area of Wind Energy (Wind power forecasting based on daily wind speed data using machine learning algorithms)

Source: 
Energy Daily News

2019 NOV 13 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Fresh data on Energy - Wind Energy are presented in a new report. According to news reporting out of Nigde, Turkey, by NewsRx editors, research stated, “Wind energy is a significant and eligible source that has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas.”

Our news journalists obtained a quote from the research from Omar Halisdemir University, “In this study, long-term wind power forecasting was performed based on daily wind speed data using five machine learning algorithms. We proposed a method based on machine learning algorithms to forecast wind power values efficiently. We conducted several case studies to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, the results showed that machine learning-based models could be applied to a location different from model-trained locations.”

According to the news editors, the research concluded: “This study demonstrated that machine learning algorithms could be successfully used before the establishment of wind plants in an unknown geographical location whether it is logical by using the model of a base location.”

For more information on this research see: Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Conversion and Management, 2019;198():111823. Energy Conversion and Management can be contacted at: Pergamon-Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Energy Conversion and Management - http://www.journals.elsevier.com/energy-conversion-and-management/)

Our news journalists report that additional information may be obtained by contacting A.S. Dokuz, Omar Halisdemir University, Faculty of Engineering, Dept. of Computer Engineering, Main Campus, Tr-51240 Nigde, Turkey. Additional authors for this research include H. Demolli, A. Ecemis and M. Gokcek.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.enconman.2019.111823. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

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