Local regression-based short-term load forecasting

This paper presents a novel method for short-term load forecasting based on local polynomial regression. Before applying the local regression, data mining algorithm selects historic load sequences satisfying known factors that are characterising required load model. Further on, the selected sequences are pre-processed with robust location estimator (M-estimator) in order to reduce serial correlation and to eliminate outliers in historic data. On pre-processed load data we applied locally a truncated Taylor expansion to approximate functional relationship between load and load-affecting factors. Two methods for selecting optimal smoothing parameters (window size and polynomial degree) for local approximations are presented in the paper. These algorithms offer to us close insight into trade-off between bias and variance of the local approximations. In that way, they are able to help in selecting smoothing parameters locally (for each local fit) to fulfil the load modelling requirements. An example is presented at the end of this paper that clearly demonstrates the main features of this method.