A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization

Xiyun Yang, Guo Fu, Yanfeng Zhang, Ning Kang and Feng Gao
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Xiyun Yang: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Guo Fu: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Yanfeng Zhang: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Ning Kang: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Feng Gao: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2017, vol. 10, issue 11, 1-15

Abstract: Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed to further improve wind power prediction performance. First, in the designed prediction interval model, the input variables are identified based on attribute significance using rough set theory. Next, the Naive Bayesian Classifier (NBC) is established to obtain the prediction power class. Finally, the upper and lower output weights of NBC are optimized segmentally by PSO, and are used to calculate the upper and lower bounds of the optimal prediction intervals. The superiority of the proposed approach is demonstrated by comparison with a Naive Bayesian model with fixed output weight, and a rough set-Naive Bayesian model with fixed output weight. It is shown that the proposed rough set-Naive Bayesian-particle swarm optimization method has higher coverage of the probabilistic prediction intervals and a narrower average bandwidth under different confidence levels.

Keywords: wind power; prediction intervals; rough set; Naive Bayesian Classifier; particle swarm optimization (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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