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Title: Classi cation of new electricity customers based on surveys and smart metering data
Authors: Viegas, Joaquim L.
Vieira, Susana M.
Melício, Rui
Mendes, Victor
Sousa, João M.C.
Keywords: Data-driven energy e ciency
Electricity customer clustering
Classi cation of new residential customers
Customer feature selection
Smart metering data
Customer surveys data
Issue Date: 1-Jul-2016
Abstract: This paper proposes a process for the classifi cation 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 classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed 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 diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.
Type: bookPart
Appears in Collections:FIS - Publicações - Capítulos de Livros

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