House and socio-demographic features vs. electricity consumption time series in main heating mode classification
Huuki, Hannu; Ruokamo, Enni; Kopsakangas-Savolainen, Maria; Belonogova, Nadezda; Sridhar, Araavind; Honkapuro, Samuli (2024-02-08)
Elsevier
08.02.2024
Huuki, H., Ruokamo, E., Kopsakangas-Savolainen, M., Belonogova, N., Sridhar, A., & Honkapuro, S. (2024). House and socio-demographic features vs. electricity consumption time series in main heating mode classification. In The Electricity Journal (Vol. 37, Issue 2, p. 107373). Elsevier BV. https://doi.org/10.1016/j.tej.2024.107373.
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202402211911
https://urn.fi/URN:NBN:fi:oulu-202402211911
Tiivistelmä
Abstract
Demand-side flexibility is crucial for integrating variable renewable energy sources cost-effectively. Home heating systems determine the potential for flexibility in individual households. We examine different approaches to classify heating systems in Finland and find that using hourly electricity consumption time series is more effective than house and socio-demographic features. Classification based on electricity consumption data achieves higher precision (0.62) and recall (0.64) than house and socio-demographic features (0.41 and 0.43, respectively). Therefore, the availability of electricity consumption time series data should be considered from a competition policy perspective due to its value in estimating flexibility potential.
Demand-side flexibility is crucial for integrating variable renewable energy sources cost-effectively. Home heating systems determine the potential for flexibility in individual households. We examine different approaches to classify heating systems in Finland and find that using hourly electricity consumption time series is more effective than house and socio-demographic features. Classification based on electricity consumption data achieves higher precision (0.62) and recall (0.64) than house and socio-demographic features (0.41 and 0.43, respectively). Therefore, the availability of electricity consumption time series data should be considered from a competition policy perspective due to its value in estimating flexibility potential.
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