Electric load forecasting in the presence of Active Demand

Active Demand (AD) is a new concept in smart grids developed within the EU project ADDRESS. It refers to the active participation of households and small commercial consumers in energy systems by means of the flexibility they can offer. Upon receiving real-time price/volume signals, consumers may find convenient to change their load profiles in return of a monetary reward. In this way, they can contribute to the provision of services to the different participants in the electricity system. Since AD causes modifications of the typical consumers' behaviour, classical load forecasting tools not considering AD signals as inputs are expected to give inaccurate results when applied to load time series including AD effects. In this paper, we study this problem by comparing the prediction performances of several linear models of the load exploiting or not AD signals as inputs. The comparison shows that enhanced prediction results can be obtained by suitably combining the use of AD inputs and the extraction of seasonal characteristics. This is demonstrated by applying the considered approaches to simulated AD effects added to real measurements, representing the aggregated load of about 60 consumers from an Italian LV network.