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

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.

Key words: wind power prediction, least square method, sliding window, harmony search

CLC Number:

TP301.6

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.

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