Disaggregated electricity forecasting using wavelet-based clustering of individual consumers

Electricity load forecasting is crucial for utilities for production planning as well as marketing offers. Recently, the increasing deployment of smart grids infrastructure requires the development of more flexible data driven forecasting methods adapting quite automatically to new data sets. We propose to build clustering tools useful for forecasting the load consumption. The idea is to disaggregate the global signal in such a way that the sum of disaggregated forecasts significantly improves the prediction of the whole global signal. The strategy is in three steps: first we cluster curves defining super-consumers, then we build a hierarchy of partitions within which the best one is finally selected with respect to a disaggregated forecast criterion. The proposed strategy is applied to a dataset of individual consumers from the French electricity provider EDF. A substantial gain of 16 % in forecast accuracy comparing to the 1 cluster approach is provided by disaggregation while preserving meaningful classes of consumers.