Behavioral similarity of residential customers using a neural network based on adaptive resonance theory

Abstract

This article presents a methodology aiming at the comprehension and analysis of the residential electricity consumption habitual behavior by means of a similarity analysis, based on the use of an ART (Adaptive Resonance Theory) neural network, which is a neural network composed of two fuzzy ART modules whose training is performed in an unsupervised mode. ART neural networks are stable and plastic and these properties, combined with the processing of essentially binary data, give the neural system a wide capacity for producing objectives that may be easily modified to satisfy requirements predetermined by consumer. The expected result is to obtain information regarding the similarity of consumers. Thus, some benefits may be derived by consumers, such as improved habits of electricity consumption and better strategies for negotiating more favorable rates, especially in the case of smart grid systems. In this new electricity sector paradigm, there is a strong consumer trend for free choice among electricity suppliers. This methodology also benefits load forecasting studies at grid points, where there is greater uncertainty, e.g., the busbars that are closest to consumers, i.e., the uncertainties in the context of the total load forecasting system are increased from the global load to the final consumer.