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Household classification using annual electricity consumption data
Hopf, Konstantin; Sodenkamp, Mariya; Kozlovskiy, Ilya; u. a. (2016): Household classification using annual electricity consumption data, in: Bamberg: opus.
Faculty/Chair:
Conference:
4th D-A-CH Conference Energieinformatik; 12-13 November 2015 ; Karlsruhe
Publisher Information:
Year of publication:
2016
Pages:
Year of first publication:
2015
Language:
English
Licence:
Abstract:
Introduction: The knowledge about household properties (such as number of inhabitants, living area, heating type, etc.) is highly desirable for utility companies to pave the way to targeted energy efficiency programs, products and services. Raising individual household data via surveys or purchasing it is expensive and time consuming, and often only a small fraction of customers participate.
Recently, data mining methods have been developed to automatically infer house-hold characteristics from smart meter consumption data. However, the slow smart metering rollout hampers practical implementation of these methods in many countries. In this work, we present a machine learning approach that reveals household properties from conventional annual electricity consumption data currently available at a large scale.
Recently, data mining methods have been developed to automatically infer house-hold characteristics from smart meter consumption data. However, the slow smart metering rollout hampers practical implementation of these methods in many countries. In this work, we present a machine learning approach that reveals household properties from conventional annual electricity consumption data currently available at a large scale.
GND Keywords: ; ; ;
Energieeffizienz
Data Mining
Stromverbrauch
Privathaushalt
Keywords:
Household classification
DDC Classification:
RVK Classification:
Type:
Conferenceobject
Activation date:
February 15, 2016
Permalink
https://fis.uni-bamberg.de/handle/uniba/39979