  Consumer Demand Response (DR) is an important research and industry problem,
which seeks to categorize, predict and modify consumer's energy consumption.
Unfortunately, traditional clustering methods have resulted in many hundreds of
clusters, with a given consumer often associated with several clusters, making
it difficult to classify consumers into stable representative groups and to
predict individual energy consumption patterns. In this paper, we present a
shape-based approach that better classifies and predicts consumer energy
consumption behavior at the household level. The method is based on Dynamic
Time Warping. DTW seeks an optimal alignment between energy consumption
patterns reflecting the effect of hidden patterns of regular consumer behavior.
Using real consumer 24-hour load curves from Opower Corporation, our method
results in a 50% reduction in the number of representative groups and an
improvement in prediction accuracy measured under DTW distance. We extend the
approach to estimate which electrical devices will be used and in which hours.

Machine Learning