Short Term Electrical Load Forecasting for Mauritius using
Artificial Neural Networks
Paper Presenter: Robert T. F. Ah King, University of Mauritius
Tina Bugwan, University of Mauritius & Robert T. F. Ah King, University of
Mauritius
The Central Electricity Board is the sole utility responsible for the
generation, transmission, distribution and sale of electrical power in
Mauritius. The country?s highest peak demand increased from 353.1 MW
in 2005 to 367.3 MW in 2006 and corresponding annual consumptions
increased from 2014.9 GWh to 2091.1 GWh and these figures are
continuously increasing every year. In this paper, different Artificial Neural
Network models are proposed for Short Term Load Forecasting (STLF) of
the Mauritian electrical load. It is shown that models based on a combined
supervised/unsupervised architecture provide better forecasting abilities
compared to those relying on supervised architectures only. Overall five
models have been investigated and implemented for this problem of STLF
for Mauritius. Model A has a total of 33 inputs comprising of 31 load data
of D-1, Day index of D-1, Month index of D-1. Model B has a total of 33
inputs comprising of 31 load data of D-1, Day index of D, Month index of
D. Model C consists of 34 inputs comprising of 31 load data of D-1, Day
index of D-1, Month index of D-1, a dummy variable for holiday (1 for
holiday, 0 for no holiday). Two models based on a combined
supervised/unsupervised architecture have been implemented, one with
the seasonal classification cluster codes (Model D) and one whose load
classification has been done on a monthly basis (Model E), that is same
day types but now with a monthly index. Out of the three models trained
using supervised learning, Model B has achieved a better forecasting
accuracy. Among the combined models Model E is more accurate.
Furthermore, Model B, having been trained using only supervised
learning, is more easily built, implemented and used in comparison to
Model E. Before obtaining the best architecture of Model E, the data was
classified using Kohonen?s Self Organizing Maps, after that the
appropriate day types were identified and the inputs were encoded again
according to the day types obtained and then finally the network was
trained. All these steps are extremely time consuming. If time were of
utmost importance for designing an accurate model, then Model B would
have been better. However for this study, the accuracy of forecasting is of
prime importance, so Model E is taken as the best. Better prediction is
achieved by clustering of the electrical load data.