Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction

Jian Yang, Xin Zhao, Haikun Wei and Kanjian Zhang
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Jian Yang: North China Branch of State Grid Corporation of China, Beijing 100053, China
Xin Zhao: Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China
Haikun Wei: Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China
Kanjian Zhang: Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China

Energies, 2019, vol. 12, issue 3, 1-12

Abstract: Wind speed prediction is the key to wind power prediction, which is very important to guarantee the security and stability of the power system. Due to dramatic changes in wind speed, it needs high-frequency sampling to describe the wind. A large number of samples are generated and affect modeling time and accuracy. Therefore, two novel active learning methods with sample selection are proposed for short-term wind speed prediction. The main objective of active learning is to minimize the number of training samples and ensure the prediction accuracy. In order to verify the validity of the proposed methods, the results of support vector regression (SVR) and artificial neural network (ANN) models with different training sets are compared. The experimental data are from a wind farm in Jiangsu Province. The simulation results show that the two novel active learning methods can effectively select typical samples. While reducing the number of training samples, the prediction performance remains almost the same or slightly improved.

Keywords: short-term wind speed prediction; active learning; support vector regression; artificial neural network (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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