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

C Sigauke, D Chikobvu


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 effective load shifting between transmission substations, scheduling of startup times of peak stations, load flow 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 nonlinearities, 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.

Full Text:


DOI: https://doi.org/10.5784/26-2-89


  • There are currently no refbacks.

ISSN 2224-0004 (online); ISSN 0259-191X (print)

Powered by OJS and hosted by Stellenbosch University Library and Information Service since 2011.


This journal is hosted by the SU LIS on request of the journal owner/editor. The SU LIS takes no responsibility for the content published within this journal, and disclaim all liability arising out of the use of or inability to use the information contained herein. We assume no responsibility, and shall not be liable for any breaches of agreement with other publishers/hosts.

SUNJournals Help