Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7113126 | Electric Power Systems Research | 2014 | 8 Pages |
Abstract
This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines auto-regressive integrated moving average (ARIMA) and neural network (NN) 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. Further, 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. Thus, the model can be an attractive tool to mitigate risks in purchasing power.
Keywords
Related Topics
Physical Sciences and Engineering
Energy
Energy Engineering and Power Technology
Authors
José C. Reston Filho, Carolina de M. Affonso, Roberto C.L. de Oliveira,