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Proceedings Paper

Short-term load forecasting study of wind power based on Elman neural network
Author(s): Xinran Tian; Jing Yu; Teng Long; Jicheng Liu
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Paper Abstract

Since wind power has intermittent, irregular and volatility nature, improving load forecasting accuracy of wind power has significant influence on controlling wind system and guarantees stable operation of power grids. This paper constructed the wind farm loading forecasting in short-term based on Elman neural network, and made a numerical example analysis. . Examples show that, using input delayed of feedback Elman neural network, can reflect the inherent laws of wind load operation better, so as to present a new idea for short-term load forecasting of wind power.

Paper Details

Date Published: 23 January 2017
PDF: 6 pages
Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103223Q (23 January 2017); doi: 10.1117/12.2265228
Show Author Affiliations
Xinran Tian, Liaoning Shihua Univ. (China)
Jing Yu, North China Electric Power Univ. (China)
Teng Long, North China Electric Power Univ. (China)
Jicheng Liu, North China Electric Power Univ. (China)


Published in SPIE Proceedings Vol. 10322:
Seventh International Conference on Electronics and Information Engineering
Xiyuan Chen, Editor(s)

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