Short-term load forecasting study of wind power based on Elman neural network

Xinran Tian; Jing Yu; Teng Long; Jicheng Liu

doi:10.1117/12.2265228

23 January 2017 Short-term load forecasting study of wind power based on Elman neural network

Xinran Tian, Jing Yu, Teng Long, Jicheng Liu

Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 103223Q (2017) https://doi.org/10.1117/12.2265228
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China

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.

Citation Download Citation

Xinran Tian, Jing Yu, Teng Long, and Jicheng Liu "Short-term load forecasting study of wind power based on Elman neural network", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103223Q (23 January 2017); https://doi.org/10.1117/12.2265228

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