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 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.
Highlights
One of the most weather-sensitive sectors of any economy is the energy sector
The forecast results obtained via the piecewise linear regression model and the multivariate adaptive regression splines (MARS) models are presented
Piecewise linear regression models were fitted for various reference temperatures in the interval 17◦C – 24◦C, without any significant improvements in the results
Summary
One of the most weather-sensitive sectors of any economy is the energy sector In this sector accurate prediction of daily peak electricity demand is very important. It provides short term forecasts which are required for dispatching and economic grid management of electric energy [1, 2, 3, 8, 16, 19, 21, 22]. The most important weather factors which affect daily peak demand (DPD) is temperature. In this paper a multivariate adaptive regression splines (MARS) model is developed and used to predict daily peak electricity demand for South Africa.
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