Abstract:
Forecasting of electricity peak demand levels is important for decision makers
in Eskom. The overall objective of this study was to develop medium
term load forecasting models which will help decision makers in Eskom for
planning of the operations of the utility company. The frequency table of
hourly daily demands was carried out and the results show that most peak
loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The
study used generalised additive models with and without tensor product interactions
to forecast electricity demand at 19:00 and 20:00 including daily
peak electricity demand. Least absolute shrinkage and selection operator
(Lasso) and Lasso via hierarchical interactions were used for variable selection
to increase the model interpretability by eliminating irrelevant variables
that are not associated with the response variable, this way also over tting
is reduced. The parameters of the developed models were estimated using
restricted maximum likelihood and penalized regression. The best models
were selected based on smallest values of the Akaike information criterion
(AIC), Bayesian information criterion (BIC) and Generalized cross validation
(GCV) along with the highest Adjusted R2. Forecasts from best models
with and without tensor product interactions were evaluated using mean absolute
percentage error (MAPE), mean absolute error (MAE) and root mean
square error (RMSE). Operational forecasting was proposed to forecast the
demand at hour 19:00 with unknown predictor variables. Empirical results
from this study show that modelling hours individually during the peak period
results in more accurate peak forecasts compared to forecasting daily
peak electricity demand. The performance of the proposed models for hour
19:00 were compared and the generalized additive model with tensor product
interactions was found to be the best tting model.
