A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria
  Neuro-fuzzy systems have attracted growing interest of researchers in various
scientific and engineering areas due to the increasing need of intelligent
systems. This paper evaluates the use of two popular soft computing techniques
and conventional statistical approach based on Box--Jenkins autoregressive
integrated moving average (ARIMA) model to predict electricity demand in the
State of Victoria, Australia. The soft computing methods considered are an
evolving fuzzy neural network (EFuNN) and an artificial neural network (ANN)
trained using scaled conjugate gradient algorithm (CGA) and backpropagation
(BP) algorithm. The forecast accuracy is compared with the forecasts used by
Victorian Power Exchange (VPX) and the actual energy demand. To evaluate, we
considered load demand patterns for 10 consecutive months taken every 30 min
for training the different prediction models. Test results show that the
neuro-fuzzy system performed better than neural networks, ARIMA model and the
VPX forecasts.
