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Open AccessArticle

Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals

Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China
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Appl. Sci. 2018, 8(2), 185; https://doi.org/10.3390/app8020185
Received: 12 December 2017 / Revised: 22 January 2018 / Accepted: 25 January 2018 / Published: 26 January 2018
(This article belongs to the Special Issue Large Grid-Connected Wind Turbines)
The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs) and prediction models using wavelet neural network (WNN). Lower upper bound estimation (LUBE) of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC) algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system. View Full-Text
Keywords: wind power forecasting; wavelet neural network; multi-objective artificial bee colony algorithm; prediction intervals wind power forecasting; wavelet neural network; multi-objective artificial bee colony algorithm; prediction intervals
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Shen, Y.; Wang, X.; Chen, J. Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals. Appl. Sci. 2018, 8, 185.

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