Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models

Ju-Yeol Ryu (), Bora Lee (), Sungho Park, Seonghyeon Hwang, Hyemin Park, Changhyeong Lee and Dohyeon Kwon
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Ju-Yeol Ryu: Institute for Advanced Engineering, Yongin 17180, Republic of Korea
Bora Lee: Institute of Health & Environment, Seoul National University, Seoul 08826, Republic of Korea
Sungho Park: Institute for Advanced Engineering, Yongin 17180, Republic of Korea
Seonghyeon Hwang: Institute for Advanced Engineering, Yongin 17180, Republic of Korea
Hyemin Park: Institute for Advanced Engineering, Yongin 17180, Republic of Korea
Changhyeong Lee: Institute for Advanced Engineering, Yongin 17180, Republic of Korea
Dohyeon Kwon: Institute for Advanced Engineering, Yongin 17180, Republic of Korea

Energies, 2022, vol. 15, issue 24, 1-14

Abstract: The rising share of renewable energy in the energy mix brings with it new challenges such as power curtailment and lack of reliable large-scale energy grid. The forecasting of wind power generation for provision of flexibility, defined as the ability to absorb and manage fluctuations in the demand and supply by storing energy at times of surplus and releasing it when needed, is important. In this study, short-term forecasting models of wind power generation were developed using the conventional time-series method and hybrid models using support vector regression (SVR) based on rolling origin recalibration. For the application of the methodology, the meteorological database from Korea Meteorological Administration and actual operating data of a wind power turbine (2.3 MW) from 1 January to 31 December 2015 were used. The results showed that the proposed SVR model has higher forecasting accuracy than the existing time-series methods. In addition, the conventional time-series model has high accuracy under proper curation of wind turbine operation data. Therefore, the analysis results reveal that data curation and weather information are as important as the model for wind power forecasting.

Keywords: wind power forecasting; time-series model; linear regression; support vector regression; rolling origin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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