Feature extraction via multiresolution analysis for short-term load forecasting

The importance of short-term load forecasting has been increasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus-load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and nonstationarity of loads are becoming worse, due to the dynamics of energy prices. Besides, the number of nodal loads to be predicted does not allow frequent interactions with load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. This paper describes two strategies for embedding the discrete wavelet transform into neural network-based short-term load forecasting. Its main goal is to develop more robust load forecasters. Hourly load and temperature data for North American and Slovakian electric utilities have been used to test the proposed methodology.