Pattern similarity-based methods for short-term load forecasting – Part 1: Principles

Abstract

Load forecasting is an integral problem in the power system operation, planning and maintenance. The article presents the principles of the pattern similarity-based methods for short-term load forecasting. A common feature of these methods is learning from the data and using similarities between patterns of the seasonal cycles of the load time series. These series are non-stationary in mean and variance, contain long run trend, many cycles of seasonal fluctuations and random noise. The new approach based on the pattern similarity and local nonparametric regression simplifies the forecasting problem and enables us to develop effective forecasting models. Several functions mapping daily cycles of the load time series into input and output patterns are defined. The assumption underlying the pattern similarity-based methods of forecasting and the way of its verification are presented. Some indicators of the strength and stability of the relationship between patterns are described. In the experimental part of the work pattern definitions and the validity of the assumption were verified using Polish power system data. The data analysis was performed specific for load time series. The results show that pattern similarity-based methods can be very useful for forecasting time series with multiple seasonal cycles.