Electric Load Pattern Classification for Demand-side Management Planning: A Hybrid Approach

Ahmed Abdulaal, Jaime H. Buitrago, and Shihab Asfour


Pattern recognition, Demand, Smart Grids, Parameter Estimation, Artificial Neural Networks, Clustering


Smart Grids require a clear understanding of consumer demand patterns. Classification of consumers according to their demand pattern is required for the effective planning of tariffs, eligibility for demand-side management (DSM) programs, energy production and transmission, as well as for security purposes. We propose a framework for classification of consumer load patterns using a hybrid system with a parameter estimation model, a clustering model and an artificial neural network (ANN). The proposed model provides an effective unbiased classification method. The process starts with generating a training data set from existing consumers without \textit{a priori} classification. The raw load data is processed through a parameter estimation model and a clustering algorithm to generate a training data set with distinct impartial classification clusters. The training data is fed to an ANN 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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