Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory

Pei Zhang, Chunping Li, Chunhua Peng and Jiangang Tian
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Pei Zhang: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Chunping Li: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Chunhua Peng: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Jiangang Tian: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China

Energies, 2020, vol. 13, issue 20, 1-13

Abstract: To improve the accuracy of ultra-short-term wind power prediction, this paper proposed a model using modified long short-term memory (LSTM) to predict ultra-short-term wind power. Because the forget gate of standard LSTM cannot reflect the correction effect of prediction errors on model prediction in ultra-short-term, this paper develops the error following forget gate (EFFG)-based LSTM model for ultra-short-term wind power prediction. The proposed EFFG-based LSTM model updates the output of the forget gate using the difference between the predicted value and the actual value, thereby reducing the impact of the prediction error at the previous moment on the prediction accuracy of wind power at this time, and improving the rolling prediction accuracy of wind power. A case study is performed using historical wind power data and numerical prediction meteorological data of an actual wind farm. Study results indicate that the root mean square error of the wind power prediction model based on EFFG-based LSTM is less than 3%, while the accuracy rate and qualified rate are more than 90%. The EFFG-based LSTM model provides better performance than the support vector machine (SVM) and standard LSTM model.

Keywords: error following forget gate-based long short-term memory; long short-term memory; ultra-short-term prediction; wind power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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