Individual, Aggregate, and Cluster-based Aggregate Forecasting of Residential Demand

While the literature has focused on large, industrial, or national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlationbased Feature Selection to select features relevant to each household. We find that the improvement provided by the Cluster-based Aggregate Forecasting strategy depends not only on the number of clusters, but more importantly on the size of the customer base.