关键词: 自组织特征映射, 特征提取, 相似日, 极限学习机, 短期负荷预测

Abstract:

In order to solve the problems of forecasting method,such as low forecasting accuracy,and long computation time in short-term electric power load forecasting,an approach to short-term load forecasting based on self-organizing feature mapping of similar days feature extraction and ELM (Extreme Learning Machine) combined method is proposed in this paper.Firstly,self-organizing neural network is used to the classification of related data.The data of the same type as that of the forecasting day are found out.Then these training samples are forecasted by ELM,which has strong ability to predict and short computing time.The power load data of one city were used for simulating.The proposed method is compared with ELM and back propagation (BP) neural network.The experimental results show that ELM method based on feature extraction of similar days has high prediction precision,good generalization performance and short running time.

Key words: self organizing feature map;feature extraction;similar days;extreme learning machine;short term load forecasting