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





Professorship/Faculty: Lehrstuhl für Wirtschaftsinformatik, insbesondere Energieeffiziente Systeme
Author(s): Hopf, Konstantin ; Sodenkamp, Mariya; Staake, Thorsten
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2020
Source/Other editions: Electronic Markets 28 (2018) 4, S. 453–473.
Year of first publication: 2018
Pages / Size: 19 Seiten
Language(s): English
Abstract: 
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.
Catchwords: Entscheidungsunterstützung ; Datenanalyse ; Energieeffizienz ; Nachhaltigkeit ; Informationssystem
Keywords: decision support systems, data analytics, energy efficiency, sustainability, green information systems
DDC Classification: 004
330
RVK Classification: ST 530
DOI: 10.20378/irb-47018
DOI: https://doi.org/10.1007/s12525-018-0290-9
Licence: Creative Commons - CC BY-SA - Attribution - ShareAlike 4.0 International
URL: https://fis.uni-bamberg.de/handle/uniba/43927
URN: urn:nbn:de:bvb:473-irb-470185
Peer Reviewed: Ja
International Distribution: Ja
Document Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/47018
Release Date: 17. January 2020

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