Electricity price forecasting is an important issue to all Market participants in
order to decide bidding strategies and to establish bilateral contracts,
maximizing their profits and minimizing their risks. Energy price typically
exhibits seasonality, high volatility and spikes. Also, energy price is influenced
by many factors such as power demand, weather, and fuel price. This work
proposes a new hybrid approach for short-term energy price prediction. This
approach combines auto-regressive integrated moving average (ARIMA) and
neural network (ANN) models in a cascaded structure and uses explanatory
variables. A two step procedure is applied. In the first step, the selected
explanatory variables are predicted. In the second one, the energy prices are
forecasted by using the explanatory variables prediction. The proposed model
considers a multi-step ahead price prediction (12 weeks-ahead) and is applied
to Brazilian market, which adopts a cost-based centralized dispatch with unique
characteristics of price behavior. The results show good ability to predict
spikes and satisfactory accuracy according to error measures and tail loss test
when compared with traditional techniques. Additionally, is proposed a classifier
model consisting of ANN and decision trees in order to explain the rules of price
formation and, together with the predictor model, acting as an attractive tool to
mitigate the risks of energy trading.
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