Abstract
In power industry and management, given the peculiarity and high complexity of the time series, it is highly requested to make models more flexible and well adapted to the data, in order to give them the ability to capture and recognize the most complex patterns, and consequently gain more accuracy in forecasting. To fulfill this aim, we define the seasonal autoregressive neural network (SAR-NN) as a dynamic feedforward artificial neural network (ANN), essentially conceived to forecast electricity prices. The SAR-NN is an ANN-based autoregressive model that considers that autoregressors are only those that are lagged by a multiple of the period p of the dominating seasonality. Moving forward this neural network, step-by-step, allows to generate multiple-steps-ahead reliable forecasts. This model is specifically designed to robustly overcome the strong seasonal effects and the other nonlinear patterns that often harm ANNs forecasting performance. The strategy is tested and compared to a number of homologous models throughout empirical experiments. As a case study, we focus on the Nord Pool Scandinavian power market, which is one of the most mature energy markets worldwide.