データマイニング手法による短期電力負荷予測

抄録

This paper proposes a method for daily maximum load forecasting in power systems. It is based on the integration of the regression tree and the artificial neural network. In this paper, the regression tree is used to extract knowledge or rules as a data-mining method. That is useful for the information processing of the complicated data. As a result, the proposed method has an advantage to clarify the cause and effect of dynamic load behavior in load forecasting. However, the regression tree does not necessarily give good prediction results in spite of good classification. Therefore, this paper proposes a method for combining the classification results of the regression tree with the multi-layer perceptron of a universal nonlinear approximator. The effectiveness of the proposed method is demonstrated in real data.