Modeling and disaggregating hourly electricity consumption in Norwegian dwellings based on smart meter data

Kipping, Anna; Trømborg, Erik

doi:10.1016/j.enbuild.2016.02.042

Cited by 32 publications

(22 citation statements)

References 12 publications

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“…While the general modeling approach for individual hourly energy consumption is described in previous work [34][35][36], this study aims to model regional aggregate hourly energy consumption by slightly adapting the existing models, including further building categories, as well as to describe how suitable input data to the aggregate models can be gathered and processed.…”

Section: Objectivesmentioning

confidence: 99%

“…In our consumption models for the service sector, we only distinguish between electric and non-electric heating and assume different shares for each county and each consumer category. In order to obtain input data to our household sector models, we combine official statistics on heating equipment from a national survey conducted in 2001 with our own assumptions, as well as survey results from a previous study [34,35]. Detailed input data for each county can be obtained from the authors.…”

Section: Heating Systemsmentioning

confidence: 99%

“…The sum of all remaining components can be interpreted as consumption for electric appliances, including electrically heated hot water tanks and space cooling equipment (i.e., electricity-bound consumption). The decomposition method is described in detail in [35].…”

Section: Decomposing Hourly Electricity Consumptionmentioning

confidence: 99%

“…The household electricity model is based on hourly electricity meter data from several hundred household customers in Buskerud country, and the corresponding cross-sectional data were gathered by a survey performed in 2013. A detailed description of how the method of Pooled Ordinary Least Squares (Pooled OLS) is applied to this panel dataset can be found in [34,35]. The models for district heat and electricity consumption in the service sector are built accordingly and are based on meter data from consumers located in Oslo, combined with cross-sectional data from the Norwegian energy label database; models for office buildings and schools are described in [36].…”

Section: Model Setupmentioning

confidence: 99%

“…For the household sector (Figure 4), aggregate hourly electricity consumption is directly calculated by using aggregate input data (i.e., total number and aggregate floor space of all dwellings in a specific region), calculated based on official statistics [37][38][39][40][41][42][43]. Different dummy variables (e.g., indicating the number of residents or the use of electric appliances and heating equipment) are given as percent shares, and are mainly based on survey response data from Buskerud county (survey items are included in [35]. Chosen input data for each county can be obtained from the corresponding author).…”

Section: Modeling Aggregate Regional Hourly Energy Consumptionmentioning

confidence: 99%

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Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Kipping

Trømborg

2017

Energies

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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.

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Section: Objectivesmentioning

confidence: 99%

Section: Heating Systemsmentioning

confidence: 99%

Section: Decomposing Hourly Electricity Consumptionmentioning

confidence: 99%

Section: Model Setupmentioning

confidence: 99%

Section: Modeling Aggregate Regional Hourly Energy Consumptionmentioning

confidence: 99%

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Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Kipping

Trømborg

2017

Energies

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Potential use of thermal energy storage for shifting cooling and heating load to off‐peak load: A case study for residential building in Canada

Erdemir

Dinçer

2020

Energy Storage

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This study presents an investigation of the potential use of thermal energy storage for shifting cooling and heating loads to off-peak hours in order to balance the electricity production and demand periods. In Canada, the main energy source for cooling and heating applications is generally electricity. Therefore, cooling and heating devices have a great impact on the electricity peak load. In this study, it is considered that heating and cooling loads on the electricity peak load periods are shifted to off-peak hours by thermal energy storage systems. At the end of this study, it is observed that the thermal energy storage has great potential for shifting electricity peak load depending on cooling and heating load to off-peak periods. The electricity peak loads can be reduced by 25% and 45% by shifting heating and cooling loads to off-peak hours and doing storage. Furthermore, the thermal energy storage systems can help reduce both cooling and heating costs in Canadian dwellings by 20% and 18%, respectively. K E Y W O R D Scooling, cost assessment, heating, thermal energy storage

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