Classification of new electricity customers based on surveys and smart metering data

Joaquim L. Viegas, Susana M. Vieira, R. Melício, V.M.F. Mendes and João M.C. Sousa

Energy, 2016, vol. 107, issue C, 804-817

Abstract: This paper proposes a process for the classification of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classification task. Firstly, the normalized representative consumption profiles of the population are derived through the clustering of data from households. Secondly, new customers are classified using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of different consumption profiles, enabling the extraction of appropriate models. The results of a case study show that the use of survey data significantly increases accuracy of the classification task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classified with only one week of metering data, with more weeks the accuracy is significantly improved. The use of model-based feature selection resulted in the use of a significantly lower number of features allowing an easy interpretation of the derived models.

Keywords: Data-driven energy efficiency; Electricity customer clustering; Classification of new residential customers; Customer feature selection; Smart metering data; Customer surveys data (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:107:y:2016:i:c:p:804-817

DOI: 10.1016/j.energy.2016.04.065

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