Research Area:  Machine Learning
With the increasing penetration of wind power in renewable energy systems, it is important to improve the accuracy of wind speed prediction. However, wind power generation has great uncertainties which make high-quality interval prediction a challenge. Existing multi-objective optimization interval prediction methods do not consider the robustness of the model. Thus, trained models for wind speed interval prediction may not be optimal for future predictions. In this paper, the prediction interval coverage probability, the prediction interval average width, and the robustness of the model are used as three objective functions for determining the optimal model of short-term wind speed interval prediction using multi-objective optimization. Furthermore, a new Stochastic Sensitivity for Prediction Intervals (SS_PIs) is proposed in this work to measure the stability and robustness of the model for interval prediction. Using wind farm data from countries on two different continents as case studies, experimental results show that the proposed method yields better prediction intervals in terms of all metrics including prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW) and SS_PIs. For example, at the prediction interval nominal confidence (PINC) of 85%, 90% and 95%, the proposed method has the best performance in all metrics of the USA wind farm dataset.
Author(s) Name:  Xuanqun Chen, Chun Sing Lai, Wing W. Y. Ng, Keda Pan, Loi Lei Lai & Cankun Zhong
Journal name:  International Journal of Machine Learning and Cybernetics
Publisher name:  Springer
Volume Information:  volume 12, pages 2579–2590 (2021)
Paper Link:   https://link.springer.com/article/10.1007/s13042-021-01340-6