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Energies 2017, 10(11), 1903; doi:10.3390/en10111903

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

School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
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Received: 19 October 2017 / Revised: 13 November 2017 / Accepted: 15 November 2017 / Published: 19 November 2017
(This article belongs to the Special Issue Wind Generators Modelling and Control)
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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. View Full-Text
Keywords: wind power; prediction intervals; rough set; Naive Bayesian Classifier; particle swarm optimization wind power; prediction intervals; rough set; Naive Bayesian Classifier; particle swarm optimization
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Yang, X.; Fu, G.; Zhang, Y.; Kang, N.; Gao, F. A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization. Energies 2017, 10, 1903.

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