Using cluster information to predict individual customer consumption

Predicting the consumption of individual customers using machine learning techniques requires a lot of time due to the size of the data and the increasing number of customers connected to the smart grid. One solution to avoid individual predictions is to cluster customers together based on similar patterns. We investigate the efficiency of using cluster information derived from our proposed Adaptive DBSCAN to predict individual consumption. We compare the results against standard ARIMA and the best found seasonal ARIMA model. Results on real-life data show an average deterioration of 30% with respect to the MAPE of the best found model when having enough clusters and using their center as baseline prediction models.