Forecasting the hourly Ontario energy price by multivariate adaptive regression splines

Abstract

Multivariate adaptive regression splines (MARS) technique is an adaptive non-parametric regression approach which has been used for various forecasting and data mining applications in recent years. This technique is more useful when a large number of explanatory variable candidates need to be considered. In this paper, the MARS technique is applied to forecast the hourly Ontario energy price (HOEP). The MARS models are developed in this work considering two scenarios for the explanatory variables. In the first scenario, the model is build based solely on the lagged values of the HOEP. In the second scenario, current and lagged values of the latest predispatch price and demand information, made available by the Ontario Independent Electricity System Operator (IESO), are also considered as explanatory variables. The forecasts generated by the developed models for high and low demand periods are significantly more accurate than the currently available forecasts for HOEP, demonstrating the MARS capability for electricity market price forecasting.