Sliding Weighted Least Square Model for Short-term Wind Power Prediction

Authors

Yanfeng Ge, 1. State Grid Liaoning Electric Power Supply Co., Ltd, Shenyang 110004, China;;
Liang Peng, 2. Jinzhou Power Supply Company, Liaoning Power Grid Co. SGCC, Jinzhou 121000, China;;
Liqun Gao, 3. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China;
Junchang Zhai, 3. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China;

Abstract

Abstract: A new approach for wind power forecasting was proposed based on sliding window weighted recursive least squares for the defect of wind power short-term prediction in the traditional prediction methods. The historical data was weighted and the perturbation caused by the historical data was ruled out in this method, which focused on the current data on the result of prediction. This made the model have the adaptability to the change of the environment data. The harmony search algorithm was used to optimize the orders and the parameters of the predict model, which could improve the precision and accuracy of the prediction result. The simulation was carried out for the real historical data of the wind farm in Liaoning Province, China to demonstrate the effectiveness of the proposed method.

Recommended Citation

Ge, Yanfeng; Peng, Liang; Gao, Liqun; and Zhai, Junchang (2020) "Sliding Weighted Least Square Model for Short-term Wind Power Prediction," Journal of System Simulation: Vol. 28: Iss. 5, Article 5.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss5/5

First Page

1031

Revised Date

2015-03-01

DOI Link

https://doi.org/

Last Page

1037

CLC

TP301.6

Recommended Citation

Ge Yanfeng, Liang Peng, Gao Liqun, Zhai Junchang. Sliding Weighted Least Square Model for Short-term Wind Power Prediction[J]. Journal of System Simulation, 2016, 28(5): 1031-1037.