基于改进K-means算法的电力短期负荷预测方法研究

Research on short-term power load forecasting method based on improved K-means algorithm

现有方法预测电力短期负荷时忽略了对其进行聚类优化处理,导致预测耗时较长,短期负荷预测精度偏低。为此,提出一种基于改进K-means算法的电力短期负荷预测方法。该方法利用改进后的K-means算法聚类处理电力负荷大数据,使用聚类后获得的训练样本构建循环神经网络RNN拓扑结构,然后通过对RNN神经网络模型设置最优权值,实现电力负荷的短期预测。实验结果表明,所提方法具有高预测效率和高短期负荷预测精准度。

The existing methods ignore the clustering optimization process when predicting the short-term load of electricity,which leads to a long prediction time and a low accuracy of short-term load prediction.Therefore,a short-term load forecasting method based on improved K-means algorithm is proposed.This method uses the improved K-means algorithm to cluster the big data of power load,uses the training samples obtained after clustering to construct the RNN topology structure of the recurrent neural network.Then the optimal weights are set for the RNN neural network model to realize short-term forecast of the power load.The experimental results show that the proposed method has high forecasting efficiency and high short-term load forecasting accuracy.