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Model for electric load profiles with high time resolution for German households
Approximately 27% of the European energy consumption is caused by the domestic sector, where 19% of the end use energy demand is caused by electric devices. To investigate the factors at play, a stochastic bottom-up model for the generation of electric load profiles is introduced in this paper. The model is designed for investigating the effects of occupant behaviour, appliance stock and efficiency on the electric load profile of an individual household. For each activity of a person in the household, an electric appliance is used, and its electricity consumption is linked to measured electric load traces with a time resolution of 10 s. Probability distributions are incorporated for when and how often an appliance is operated. Duration of operation is given as probability density conditional on the start time. Shared use of an appliance by multiple persons is included in the model. Seasonal effects are considered by using changing probability sets during the course of the year. For validation, seven subgroups, which reflect typical household configurations, were formed and tested against measured field data from 430 households in 9 different cities across Germany. The results showed an accuracy of 91% and a correlation of up to 0.98.
Model for electric load profiles with high time resolution for German households
Approximately 27% of the European energy consumption is caused by the domestic sector, where 19% of the end use energy demand is caused by electric devices. To investigate the factors at play, a stochastic bottom-up model for the generation of electric load profiles is introduced in this paper. The model is designed for investigating the effects of occupant behaviour, appliance stock and efficiency on the electric load profile of an individual household. For each activity of a person in the household, an electric appliance is used, and its electricity consumption is linked to measured electric load traces with a time resolution of 10 s. Probability distributions are incorporated for when and how often an appliance is operated. Duration of operation is given as probability density conditional on the start time. Shared use of an appliance by multiple persons is included in the model. Seasonal effects are considered by using changing probability sets during the course of the year. For validation, seven subgroups, which reflect typical household configurations, were formed and tested against measured field data from 430 households in 9 different cities across Germany. The results showed an accuracy of 91% and a correlation of up to 0.98.
Model for electric load profiles with high time resolution for German households
Fischer, David (author) / Härtl, Andreas / Wille-Haussmann, Bernhard
Energy and buildings ; 92
2015
Article (Journal)
English
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