Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock
Sound estimates of future heat and electricity demand with high temporal and spatial resolution are needed for energy system planning, grid design, and evaluating demand-side management options and polices on regional and national levels. In this study, smart meter data on electricity consumption in...
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Online Access: | https://www.mdpi.com/1996-1073/11/1/78 |
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doaj-a6246d47a5484776b819e303464ca4912020-11-25T00:46:08ZengMDPI AGEnergies1996-10732017-12-011117810.3390/en11010078en11010078Modeling Aggregate Hourly Energy Consumption in a Regional Building StockAnna Kipping0Erik Trømborg1Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, NorwaySound estimates of future heat and electricity demand with high temporal and spatial resolution are needed for energy system planning, grid design, and evaluating demand-side management options and polices on regional and national levels. In this study, smart meter data on electricity consumption in buildings are combined with cross-sectional building information to model hourly electricity consumption within the household and service sectors on a regional basis in Norway. The same modeling approach is applied to model aggregate hourly district heat consumption in three different consumer groups located in Oslo. A comparison of modeled and metered hourly energy consumption shows that hourly variations and aggregate consumption per county and year are reproduced well by the models. However, for some smaller regions, modeled annual electricity consumption is over- or underestimated by more than 20%. Our results indicate that the presented method is useful for modeling the current and future hourly energy consumption of a regional building stock, but that larger and more detailed training datasets are required to improve the models, and more detailed building stock statistics on regional level are needed to generate useful estimates on aggregate regional energy consumption.https://www.mdpi.com/1996-1073/11/1/78energy systemssmart meter datahourly electricity consumptionpanel datadistrict heat |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anna Kipping Erik Trømborg |
spellingShingle |
Anna Kipping Erik Trømborg Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock Energies energy systems smart meter data hourly electricity consumption panel data district heat |
author_facet |
Anna Kipping Erik Trømborg |
author_sort |
Anna Kipping |
title |
Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock |
title_short |
Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock |
title_full |
Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock |
title_fullStr |
Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock |
title_full_unstemmed |
Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock |
title_sort |
modeling aggregate hourly energy consumption in a regional building stock |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2017-12-01 |
description |
Sound estimates of future heat and electricity demand with high temporal and spatial resolution are needed for energy system planning, grid design, and evaluating demand-side management options and polices on regional and national levels. In this study, smart meter data on electricity consumption in buildings are combined with cross-sectional building information to model hourly electricity consumption within the household and service sectors on a regional basis in Norway. The same modeling approach is applied to model aggregate hourly district heat consumption in three different consumer groups located in Oslo. A comparison of modeled and metered hourly energy consumption shows that hourly variations and aggregate consumption per county and year are reproduced well by the models. However, for some smaller regions, modeled annual electricity consumption is over- or underestimated by more than 20%. Our results indicate that the presented method is useful for modeling the current and future hourly energy consumption of a regional building stock, but that larger and more detailed training datasets are required to improve the models, and more detailed building stock statistics on regional level are needed to generate useful estimates on aggregate regional energy consumption. |
topic |
energy systems smart meter data hourly electricity consumption panel data district heat |
url |
https://www.mdpi.com/1996-1073/11/1/78 |
work_keys_str_mv |
AT annakipping modelingaggregatehourlyenergyconsumptioninaregionalbuildingstock AT eriktrømborg modelingaggregatehourlyenergyconsumptioninaregionalbuildingstock |
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1725266730356310016 |