Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach

Peter Josef Nageler, Andreas Koch, Franz Mauthner, Ingo Leusbrock, Thomas Mach, Christoph Hochenauer, Richard Heimrath

Publikation: Beitrag in Fachzeitschrift/ZeitungArtikel

Abstract

Thermal energy demand represents over 30% of overall energy consumption and thus is a vital target for the decarbonisation of our energy supply. This fact is the reason why dynamic thermal modelling of buildings, components and infrastructure has been gaining increasing importance in the context of the transformation of existing energy systems into smart energy systems. For the first time, this study presents a comparison of two major bottom-up approaches to modelling urban neighbourhoods: a physical modelling approach and a data-driven approach. The physical method is represented by a GIS-based automated modelling approach with detailed dynamic building simulation in IDA ICE. In the data-driven approach, an energy signature is applied, which uses a non-linear data-driven method. These two methods were validated on the basis of a multi-family house, an office building and a residential area with 34 buildings by means of detailed multi-zone building simulation and measurement data. The simulation results show that both applied approaches are applicable in these cases within good agreement compared to the measurement data (physical/data-driven: multi-family house (RN_RMSE(%) = 14.7/9.3; R2 = 0.68/0.87), office building (RN_RMSE(%) = 7.6/5.4; R2 = 0.92/0.96) and residential area (RN_RMSE(%) = 8.2/4.8; R2 = 0.92/0.97)). Finally, the fields of application of both approaches are discussed. A major finding here is that the energy signature shows slightly better results at deriving the load profile when the measured heating demand is present from a previous heating season. Furthermore, the number of buildings does not affect the duration of a simulation because cumulative user profiles are used. Whereas the simulation duration of the physical approach depends essentially on the size of the investigated area. The physical approaches have the advantage of being able to include densification or renovation scenarios, demand forecasting and coupled simulations of buildings and smart energy systems of neighbourhoods.
Spracheenglisch
Seiten333-343
Seitenumfang11
ZeitschriftEnergy and buildings
Band179
DOIs
StatusVeröffentlicht - 15 Nov 2018

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Office buildings
Decarbonization
Heating
Time and motion study
Thermal energy
Densification
Geographic information systems
Energy utilization
Hot Temperature

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    Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach. / Nageler, Peter Josef; Koch, Andreas; Mauthner, Franz; Leusbrock, Ingo; Mach, Thomas; Hochenauer, Christoph; Heimrath, Richard.

    in: Energy and buildings, Band 179, 15.11.2018, S. 333-343.

    Publikation: Beitrag in Fachzeitschrift/ZeitungArtikel

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    title = "Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach",
    abstract = "Thermal energy demand represents over 30{\%} of overall energy consumption and thus is a vital target for the decarbonisation of our energy supply. This fact is the reason why dynamic thermal modelling of buildings, components and infrastructure has been gaining increasing importance in the context of the transformation of existing energy systems into smart energy systems. For the first time, this study presents a comparison of two major bottom-up approaches to modelling urban neighbourhoods: a physical modelling approach and a data-driven approach. The physical method is represented by a GIS-based automated modelling approach with detailed dynamic building simulation in IDA ICE. In the data-driven approach, an energy signature is applied, which uses a non-linear data-driven method. These two methods were validated on the basis of a multi-family house, an office building and a residential area with 34 buildings by means of detailed multi-zone building simulation and measurement data. The simulation results show that both applied approaches are applicable in these cases within good agreement compared to the measurement data (physical/data-driven: multi-family house (RN_RMSE({\%}) = 14.7/9.3; R2 = 0.68/0.87), office building (RN_RMSE({\%}) = 7.6/5.4; R2 = 0.92/0.96) and residential area (RN_RMSE({\%}) = 8.2/4.8; R2 = 0.92/0.97)). Finally, the fields of application of both approaches are discussed. A major finding here is that the energy signature shows slightly better results at deriving the load profile when the measured heating demand is present from a previous heating season. Furthermore, the number of buildings does not affect the duration of a simulation because cumulative user profiles are used. Whereas the simulation duration of the physical approach depends essentially on the size of the investigated area. The physical approaches have the advantage of being able to include densification or renovation scenarios, demand forecasting and coupled simulations of buildings and smart energy systems of neighbourhoods.",
    keywords = "Dynamic urban building energy modelling, Data-driven approach, Physical approach, Heating demand forecasting",
    author = "Nageler, {Peter Josef} and Andreas Koch and Franz Mauthner and Ingo Leusbrock and Thomas Mach and Christoph Hochenauer and Richard Heimrath",
    year = "2018",
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    T1 - Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach

    AU - Nageler,Peter Josef

    AU - Koch,Andreas

    AU - Mauthner,Franz

    AU - Leusbrock,Ingo

    AU - Mach,Thomas

    AU - Hochenauer,Christoph

    AU - Heimrath,Richard

    PY - 2018/11/15

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    N2 - Thermal energy demand represents over 30% of overall energy consumption and thus is a vital target for the decarbonisation of our energy supply. This fact is the reason why dynamic thermal modelling of buildings, components and infrastructure has been gaining increasing importance in the context of the transformation of existing energy systems into smart energy systems. For the first time, this study presents a comparison of two major bottom-up approaches to modelling urban neighbourhoods: a physical modelling approach and a data-driven approach. The physical method is represented by a GIS-based automated modelling approach with detailed dynamic building simulation in IDA ICE. In the data-driven approach, an energy signature is applied, which uses a non-linear data-driven method. These two methods were validated on the basis of a multi-family house, an office building and a residential area with 34 buildings by means of detailed multi-zone building simulation and measurement data. The simulation results show that both applied approaches are applicable in these cases within good agreement compared to the measurement data (physical/data-driven: multi-family house (RN_RMSE(%) = 14.7/9.3; R2 = 0.68/0.87), office building (RN_RMSE(%) = 7.6/5.4; R2 = 0.92/0.96) and residential area (RN_RMSE(%) = 8.2/4.8; R2 = 0.92/0.97)). Finally, the fields of application of both approaches are discussed. A major finding here is that the energy signature shows slightly better results at deriving the load profile when the measured heating demand is present from a previous heating season. Furthermore, the number of buildings does not affect the duration of a simulation because cumulative user profiles are used. Whereas the simulation duration of the physical approach depends essentially on the size of the investigated area. The physical approaches have the advantage of being able to include densification or renovation scenarios, demand forecasting and coupled simulations of buildings and smart energy systems of neighbourhoods.

    AB - Thermal energy demand represents over 30% of overall energy consumption and thus is a vital target for the decarbonisation of our energy supply. This fact is the reason why dynamic thermal modelling of buildings, components and infrastructure has been gaining increasing importance in the context of the transformation of existing energy systems into smart energy systems. For the first time, this study presents a comparison of two major bottom-up approaches to modelling urban neighbourhoods: a physical modelling approach and a data-driven approach. The physical method is represented by a GIS-based automated modelling approach with detailed dynamic building simulation in IDA ICE. In the data-driven approach, an energy signature is applied, which uses a non-linear data-driven method. These two methods were validated on the basis of a multi-family house, an office building and a residential area with 34 buildings by means of detailed multi-zone building simulation and measurement data. The simulation results show that both applied approaches are applicable in these cases within good agreement compared to the measurement data (physical/data-driven: multi-family house (RN_RMSE(%) = 14.7/9.3; R2 = 0.68/0.87), office building (RN_RMSE(%) = 7.6/5.4; R2 = 0.92/0.96) and residential area (RN_RMSE(%) = 8.2/4.8; R2 = 0.92/0.97)). Finally, the fields of application of both approaches are discussed. A major finding here is that the energy signature shows slightly better results at deriving the load profile when the measured heating demand is present from a previous heating season. Furthermore, the number of buildings does not affect the duration of a simulation because cumulative user profiles are used. Whereas the simulation duration of the physical approach depends essentially on the size of the investigated area. The physical approaches have the advantage of being able to include densification or renovation scenarios, demand forecasting and coupled simulations of buildings and smart energy systems of neighbourhoods.

    KW - Dynamic urban building energy modelling

    KW - Data-driven approach

    KW - Physical approach

    KW - Heating demand forecasting

    U2 - 10.1016/j.enbuild.2018.09.034

    DO - 10.1016/j.enbuild.2018.09.034

    M3 - Article

    VL - 179

    SP - 333

    EP - 343

    JO - Energy and buildings

    T2 - Energy and buildings

    JF - Energy and buildings

    SN - 0378-7788

    ER -