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Modelling of household electricity consumption with the aid of computational intelligence methods

Modelling of household electricity consumption with the aid of computational intelligence methods The installation of smart meters for electricity consumption monitoring is common practice in many countries. Such meters usually provide information for the temporal variation of electricity consumption-related parameters, at an aggregated (household) level. In some cases, such meters may monitor individual appliances, or appliance groups installed in household departments. In the current study, a Computational Intelligence approach is used to analyse and model appliance group electricity consumption and to investigate the best possible computational approaches for improving consumption model performance. For this purpose, meta-features are used, a new feature prioritization method is introduced and a set of selected algorithms is employed. Results indicate an improvement in modelling capacity and an ability to construct models that effectively perform partial electricity consumption disaggregation. Overall, such methods may be used for the support of household electricity consumption modelling and for related demand management. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Building Energy Research Taylor & Francis

Modelling of household electricity consumption with the aid of computational intelligence methods

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Modelling of household electricity consumption with the aid of computational intelligence methods

Abstract

The installation of smart meters for electricity consumption monitoring is common practice in many countries. Such meters usually provide information for the temporal variation of electricity consumption-related parameters, at an aggregated (household) level. In some cases, such meters may monitor individual appliances, or appliance groups installed in household departments. In the current study, a Computational Intelligence approach is used to analyse and model appliance group electricity...
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Publisher
Taylor & Francis
Copyright
© 2017 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1756-2201
eISSN
1751-2549
DOI
10.1080/17512549.2017.1314831
Publisher site
See Article on Publisher Site

Abstract

The installation of smart meters for electricity consumption monitoring is common practice in many countries. Such meters usually provide information for the temporal variation of electricity consumption-related parameters, at an aggregated (household) level. In some cases, such meters may monitor individual appliances, or appliance groups installed in household departments. In the current study, a Computational Intelligence approach is used to analyse and model appliance group electricity consumption and to investigate the best possible computational approaches for improving consumption model performance. For this purpose, meta-features are used, a new feature prioritization method is introduced and a set of selected algorithms is employed. Results indicate an improvement in modelling capacity and an ability to construct models that effectively perform partial electricity consumption disaggregation. Overall, such methods may be used for the support of household electricity consumption modelling and for related demand management.

Journal

Advances in Building Energy Research – Taylor & Francis

Published: Jan 2, 2018

Keywords: Household electricity consumption; smart metering; computational intelligence; partial disaggregation

References

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