Short -term Prediction of Wind Power Based on IDE-WNN and Probabilistic Evaluation

Authors

Zengliang Liu, 1. Department of Electrical Engineering, Tongling University, Tongling 244000, China;;
Songlin Zhou, 1. Department of Electrical Engineering, Tongling University, Tongling 244000, China;;
Tongxu Zhou, 2. Department of Mechanical and electronic engineering, West Anhui University, Luan 237012, China;

Abstract

Abstract: Wind power prediction usually concludes determined and uncertainly prediction. The former puts important on prediction accuracy and the later focus on the the risk of prediction results. For increase the prediction accuracy, an improved differential evolution algorithm was applied to widely search the optimal solution of wavelet neural network in different directions. By calculating joint conditional probability of wind power predictions about prediction error and power fluctuation, the risk of prediction results could be more fully assessed. The effectiveness of the proposed method was verified by simulation experiments.

Recommended Citation

Liu, Zengliang; Zhou, Songlin; and Zhou, Tongxu (2020) "Short -term Prediction of Wind Power Based on IDE-WNN and Probabilistic Evaluation," Journal of System Simulation: Vol. 28: Iss. 2, Article 31.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss2/31

First Page

476

Revised Date

2014-12-15

DOI Link

https://doi.org/

Last Page

482

CLC

TP391.9

Recommended Citation

Liu Zengliang, Zhou Songlin, Zhou Tongxu. Short -term Prediction of Wind Power Based on IDE-WNN and Probabilistic Evaluation[J]. Journal of System Simulation, 2016, 28(2): 476-482.