Enhancing energy efficiency in the residential sector with smart meter data analytics

Faculty/Professorship: Information Systems and Energy Efficient Systems  
Author(s): Hopf, Konstantin  ; Sodenkamp, Mariya; Staake, Thorsten
Title of the Journal: Electronic Markets
ISSN: 1019-6781
Publisher Information: Berlin ; Heidelberg : Springer
Year of publication: 2017
Issue: (2018), First Online: 17 March 2018
Pages: 21 ; Online-Ressource
Source/Other editions: Postprint erschienen: Otto-Friedrich-Universität Bamberg, 2020
Language(s): English
DOI: 10.1007/s12525-018-0290-9
URL: https://fis.uni-bamberg.de/handle/uniba/47018
Tailored energy efficiency campaigns that make use of household-specific information can trigger substantial energy savings in the residential sector. The information required for such campaigns, however, is often missing. We show that utility companies can extract that information from smart meter data using machine learning. We derive 133 features from smart meter and weather data and use the Random Forest classifier that allows us to recognize 19 household classes related to 11 household characteristics (e.g., electric heating, size of dwelling) with an accuracy of up to 95% (69% on average). The results indicate that even datasets with an hourly or daily resolution are sufficient to impute key household characteristics with decent accuracy and that data from different yearly seasons does not considerably influence the classification performance. Furthermore, we demonstrate that a small training data set consisting of only 200 households already reaches a good performance. Our work may serve as benchmark for upcoming, similar research on smart meter data and provide guidance for practitioners for estimating the efforts of implementing such analytics solutions.
GND Keywords: Entscheidungsunterstützung ; Datenanalyse ; Energieeffizienz ; Nachhaltigkeit ; Informationssystem
Keywords: Green information systems, Decision support systems, Data analytics, Energy efficiency, Classification
DDC Classification: 004 Computer science  
330 Economics  
RVK Classification: ST 530   
Peer Reviewed: Ja
International Distribution: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/43927
Year of publication: 18. June 2018