Application of associated clustering and classification method in electric power load forecasting

Abstract

Clustering and classification are two important research areas of data mining. Classification needs related prior-knowledge, while clustering normally finds its own inherent characteristics from the data based on similarity measure. In the process of power load forecasting, the results of classification and clustering are inconsistent. For this problem, this paper propose the definition of associated matrix and on that basis propose associated clustering-classification algorithm. This algorithm is applied to data sample classification for power load prediction, the experiment show that the classification results obtained by our method are more reliable.