﻿ 基于K-均值聚类的风能短期功率预测

# 基于K-均值聚类的风能短期功率预测Short-Term Wind Power Prediction Based on K-Means Clustering Algorithm

Abstract:
Forecasting the output power of wind farm play a vital role in the reducing of the running cost of wind power plants and the reasonable arrangements for the dispatch of power systems. Improving the prediction accuracy of wind power can contribute to lower the detrimental impact of wind power plants on power grid as well as improve the competitiveness of wind power plants against others in electricity markets. As in the establishment of short-term wind power forecasting model, the sample selection has a greater impact on prediction accuracy, so the study of sample selection method is very important. In this paper, a new method is proposed in which the history wind power data is clusterred by K-means algorithm, data is classified through the LVQ net and the prediction model of wind power is established with the least-squares method. The practical application shows that the method can be utilized to predict the wind power effectively and precisely, and it is quite significant for the regulation of wind power.

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