Nonparametric trend model for short term electricity demand forecasting

In this paper, we present a novel nonparametric algorithm for short term electricity demand forecasting. The algorithm is based on local linear regression using sliding window with variable length. The method for selecting optimal window length for each local fit offers close insight into trade-off between bias and standard deviation of local regressions. Optimal window length is selected for each value in the load time-series: large window for linear change of load to reduce variability and small window when load departs from linear function to control bias. In the presented algorithm local linear regression is used to estimate trend component of the load time series and to forecast trend component by extrapolating with the fitted local linear function. Some features of the algorithm are demonstrated in the paper using examples from the historic load data recorded in the Namibian Power Utility.