Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Chia-Sheng Tu, Chih-Ming Hong, Hsi-Shan Huang and Chiung-Hsing Chen
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Chia-Sheng Tu: College of Intelligence Robot, Fuzhou Polytechnic, Fuzhou 350108, China
Chih-Ming Hong: Department of Electronic Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811213, Taiwan
Hsi-Shan Huang: College of Intelligence Robot, Fuzhou Polytechnic, Fuzhou 350108, China
Chiung-Hsing Chen: Department of Electronic Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811213, Taiwan

Energies, 2020, vol. 13, issue 23, 1-18

Abstract: This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

Keywords: least squares support vector machine (LSSVM); enhanced bee swarm optimization (EBSO); wind speed; wind power; forecasting (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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