Hopf, KonstantinKonstantinHopf0000-0002-5452-0672Sodenkamp, MariyaMariyaSodenkampKozlovskiy, IlyaIlyaKozlovskiyStaake, ThorstenThorstenStaake2019-09-192016-02-152016https://fis.uni-bamberg.de/handle/uniba/39979Introduction: 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.engHousehold classification004333.7conferenceobjecturn:nbn:de:bvb:473-opus4-458080