Reconstructing clusters for preconditioned short‐term load forecasting

AbstractThis paper presents a new preconditioned method for short‐term load forecasting that focuses on more accurate predicted value. In recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short‐term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. In this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen‐based neural network. That enhances a short‐term load forecasting model at each reconstructed cluster. The proposed method is successfully applied to real data of one‐step ahead daily maximum load forecasting. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 161(1): 26–33, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20300