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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jian Yang & Xin Zhao & Haikun Wei & Kanjian Zhang, 2019. "Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction," Energies, MDPI, vol. 12(3), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:337-:d:199870
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    References listed on IDEAS

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    Cited by:

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    2. Qin Chen & Yan Chen & Xingzhi Bai, 2020. "Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind," Energies, MDPI, vol. 13(21), pages 1-23, October.

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