Long-term energy load forecasting using Auto-Regressive and approximating Support Vector Regression

Several techniques can be retrieved in literature, which cope with the problem of energy load forecasting in the short-term framework, that is hours up to few days ahead. However, in order to properly schedule operative conditions including energy purchasing and generation, fuel supply, and infrastructure development and maintenance, a long-term prediction is of crucial importance. In this framework, the generalization of the short-term techniques to this more complex problem is usually characterized by a scarce performance. In this paper, we present an innovative method, which exploits the Savitzky-Golay filter and the Support Vector Regression algorithm in order to reliably predict the energy consumption in the long-term framework: the extrapolation ability of our proposal is validated on a real-world problem.