Reducing the cost of wind resource assessment: using a regional wind power forecasting method for assessment

Ozkan M. B. , KARAGÖZ P.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, vol.45, no.9, pp.13182-13197, 2021 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 9
  • Publication Date: 2021
  • Doi Number: 10.1002/er.6645
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Page Numbers: pp.13182-13197
  • Keywords: data mining, regional wind power forecasting, wind map, wind resource assessment


Wind energy, with its high potential, has an important place among renewable energy sources. Therefore, the number of investments on wind energy is increasing with new turbine technologies and solutions. For the investors of these technologies, how to determine the location of wind turbines for such investments is a challenging and critical problem. Wind maps of the country are a useful resource to determine high potential locations. However, such maps often present an overview to provide the general picture, and there is a need for a more detailed analysis, specifically on the potential plant area. In this paper, we investigate the use of a recent regional wind power forecast method to determine the wind power potential. By applying the RegionalSHWIP on a potential plant area, investors can obtain the preliminary power prediction, hence the wind potential of the area, without any financial cost recurring for measurements and with negligible manhour for computation. The wind resource assessments of the RegionalSHWIP have been analyzed in comparison to the wind maps of four provinces from different regions of Turkey. In all of these test provinces, it has been observed that the model produces results compatible with the values in the wind potential atlas. In addition, the proposed model has been run on 16 online power plants by selecting 4 different real wind power plants from each province, pretending that these power plants are offline. It has been observed that the total MWh values obtained from the actual generation of these power plants and the total MWh values obtained by estimation are also compatible and the results have been found to support the model.