Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
The electricity footprint of household activities - implications for demand models
It is an intuitive assumption that some activities require more energy than others. Bottom-up energy demand models therefore use time-use data to inform the timing of energy use. In this paper we present some empirical evidence to test the strength of this assumption. Using data that simultaneously captures household activities and their coinciding electricity consumption, it is possible to relate one to the other. We validate the temporal accuracy of the approach with the example of reporting hot drinks and the distinct signature of kettle usage. Despite good data accuracy, the predictive power of reported activities for electricity use is modest. At time when activities that would subjectively be associated with high energy consumption are reported, electricity use is only about 7% higher than at times with activities of low energy association. For single occupant households the link is stronger with more than 30% difference between the two activity categories. We conclude that demand models may need to take account of diversity and complexity in multi-occupant households and that more sophisticated regression techniques may be required to improve demand predictions based on time-use data.
The electricity footprint of household activities - implications for demand models
It is an intuitive assumption that some activities require more energy than others. Bottom-up energy demand models therefore use time-use data to inform the timing of energy use. In this paper we present some empirical evidence to test the strength of this assumption. Using data that simultaneously captures household activities and their coinciding electricity consumption, it is possible to relate one to the other. We validate the temporal accuracy of the approach with the example of reporting hot drinks and the distinct signature of kettle usage. Despite good data accuracy, the predictive power of reported activities for electricity use is modest. At time when activities that would subjectively be associated with high energy consumption are reported, electricity use is only about 7% higher than at times with activities of low energy association. For single occupant households the link is stronger with more than 30% difference between the two activity categories. We conclude that demand models may need to take account of diversity and complexity in multi-occupant households and that more sophisticated regression techniques may be required to improve demand predictions based on time-use data.
The electricity footprint of household activities - implications for demand models
Grunewald, P (Autor:in) / Diakonova, M (Autor:in)
24.10.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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