Jaime Buitrago, Ahmed Abdulaal, and Shihab Asfour
Pattern recognition, demand, framework, parameter estimation, ANN, clustering
The smart grid allows for capturing vital information about consumer demand patterns through smart metering. This enables utilities to effectively plan new tariffs, demand-side management programs, energy production and transmission, as well as improve the overall grid security. However, the immense amount of data makes the analysis process increasingly difficult. Therefore, grouping of consumer data with similar demand patterns is necessary. In this paper, we propose a hybrid framework for the unbiased classification of consumer demand patterns. The hybrid system consists of a parameter estimation model, a clustering model, and an artificial neural network (ANN). The smart metering data are first processed through a parameter estimation model and a clustering algorithm producing distinct impartial classification clusters. The results are then fed to an ANN as training data for learning. Once the load patterns are learned, the model can be used to further classify new consumers into a demand pattern. The ANN provides fast and accurate clustering performance without underlying assumptions about shape or class. An analysis of the optimal number of clusters is presented. Results indicate that clusters with distinguishable characteristics are achieved and we demonstrate how regulators can make use of this method in demand curtailment planning.
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