Exogenous Data for Load Forecasting: A Review

Abstract

Electrical power load forecasting defines strategies for utilities, power producers and individuals that participate in a smart grid. While it is well established in planning processes for production and utilities, the importance of accurate forecasting increases for individuals. The ongoing deregulation of the electricity market enables energy trading by individuals, requiring an accurate estimation of the production and consumption. Research on forecast for aggregated demand shows that including features for the forecast from sources, called exogenous, additional to the purely historical consumption data allows to obtain higher accuracy. In fact, their usage demonstrated to be able to explain the large variability observed in the power demand, taking into account the individual influences. Anyway, the influence of exogenous data is hardly investigated for individual forecasting, due to the minor prevalence of this analysis to date. This review shows the benefit of exogenous data usage and the necessity of detailed research on the input features and their influence on detailed, individual level, forecasts of power demand. Eventually, this contribution is concluded by the presentation of open issues and research directions for electric smart communities that the authors would like to address.