Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average

Several studies focus on improving forecasting techniques to capture multiple patterns in time series. The evolution of computing hardware has made possible to solve complex equations with large amount of data, such as the one used in neural networks. On the other hand, time series methods such as ARIMA (Autoregressive Integrated Moving Average) could also have a good approximation with low computational resources. Nonetheless, to improve the ARIMA approximations, it could be combined with other techniques such as Wavelet Transform or Fourier Transform. Therefore, this work evaluates the appropriate utilization to make predictions for different time horizons (2, 5 and 10 years) and different time frequencies (days, months, and years) using artificial neural network, ARIMA combined with Wavelet Transform, or Fourier Transform. The results show that Artificial Neural Networks provides a better approach for short-term horizons considering either time frequency, ARIMA with Fourier Transform has the best approximation for the monthly time series and either time horizons and ARIMA with Wavelet Transform has the best approximation for medium-term and long-term periods with either time frequency.