Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach

Abstract

Accurate prediction of daily peak load demand is very important for decision makers in the energy sector. This helps in the determination of consistent and reliable supply schedules during peak periods. Accurate short term load forecasts enable eective load shifting between transmission substations, scheduling of startup times of peak stations, load  ow analysis and power system security studies. A multivariate adaptive regression splines  (MARS) modelling approach towards daily peak electricity load forecasting in South Africa is presented in this paper for the period 2000 to 2009. MARS is a non-parametric multivariate regression method which is used in high-dimensional problems with complex model structures, such as nonlin-earities, interactions and missing data, in a straight forward manner and produces results which may easily be explained to management. The models developed in this paper consist of components that represent calendar and meteorological data. The performances of the models are evaluated by comparing them to a piecewise linear regression model. The results from the study show that the MARS models achieve better forecast accuracy.

Key words: MARS, temperature, peak electricity demand forecasting, piecewise linear regression.