An Evolutionary Multiobjective Knee-Based Lower Upper Bound Estimation Method for Wind Speed Interval Forecast

Li, Kaiwen1; Zhang, Tao1; Wang, Rui1; Wang, Ling2; Ishibuchi, Hisao3

2022-10-01

10.1109/TEVC.2021.3122191

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION   影响因子和分区

1089-778X

1941-0026

Due to the high variability and uncertainty of the wind speed, an interval forecast can provide more information for decision makers to achieve a better energy management compared to the traditional point forecast. In this article, a knee-based lower upper bound estimation method (K-LUBE) is proposed to construct wind speed prediction intervals (PIs). First, we analyze the underlying limitations of traditional direct interval forecast methods, i.e., their obtained PIs often fail to achieve a good balance between the interval width and the coverage probability. K-LUBE resolves the difficulty based on a multiobjective optimization framework in conjunction with a knee selection criterion. Specifically, a PI-NSGA-II multiobjective optimization algorithm is designed to obtain a set of Pareto-optimal solutions. A parameter transfer and a sample training strategies are developed to significantly improve the convergence speed of the optimization procedure. Then, the knee selection criterion is introduced to select the best tradeoff solution among the obtained solutions. In comparison with traditional methods, this method can always provide a reliable PI for decision makers. The procedure is automatic and requires no parameter to be specified in advance, making it more practical for use. The effectiveness of the proposed K-LUBE method is demonstrated through extensive comparisons with four traditional direct interval forecast methods and four classical benchmark models.

Forecasting Wind speed Wind forecasting Pareto optimization Artificial neural networks Probabilistic logic Estimation Knee multiobjective optimization neural network (NN) prediction interval (PI) wind speed

SCI

英语

其他

National Science Fund for Outstanding Young Scholars[62122093] ; National Natural Science Foundation of China[72071205] ; Ji-Hua Laboratory Scientific Project[X210101UZ210]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000862385200020

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

COMPUTER SCIENCE

Web of Science

1.Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China
2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China

Li, Kaiwen,Zhang, Tao,Wang, Rui,et al. An Evolutionary Multiobjective Knee-Based Lower Upper Bound Estimation Method for Wind Speed Interval Forecast[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2022,26(5):1030-1042.

Li, Kaiwen,Zhang, Tao,Wang, Rui,Wang, Ling,&Ishibuchi, Hisao.(2022).An Evolutionary Multiobjective Knee-Based Lower Upper Bound Estimation Method for Wind Speed Interval Forecast.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,26(5),1030-1042.

Li, Kaiwen,et al."An Evolutionary Multiobjective Knee-Based Lower Upper Bound Estimation Method for Wind Speed Interval Forecast".IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 26.5(2022):1030-1042.