Title :
Energy price forecasting in the North Brazilian market using NN - ARIMA model and explanatory variables
Author :
Filho, Jose Carlos R. ; Affonso, Carolina M. ; Oliveira, Roberto C. L.
Author_Institution :
Amazon Data Inst. - IDAAM, Manaus, Brazil
Abstract :
This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines ARIMA and NN models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. The prediction time horizon is 12 weeks-ahead and is applied to the North Brazilian submarket, which adopts a cost-based model with unique characteristics of price behavior. The proposed strategy is compared with traditional techniques like ARIMA and NN and the results show satisfactory accuracy and good ability to predict spikes. Thus, the model can be an attractive tool to mitigate risks in purchasing power.
Keywords :
autoregressive moving average processes; load forecasting; neural nets; power engineering computing; power markets; ARIMA models; NN models; North Brazilian submarket; cost-based model; energy price forecasting; explanatory variables prediction; short-term energy price prediction; Artificial neural networks; Electricity; Electricity supply industry; Forecasting; Predictive models; Principal component analysis; ARIMA; NN; electricity market; energy price forecasting; explanatory variable; hybrid model; multi-step-ahead;
Conference_Titel :
Computational Intelligence for Engineering Solutions (CIES), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CIES.2014.7011847