Use case "Comparative analysis of different methodologies and datasets for Energy Performance Labelling of buildings"

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In the following sections we provide more information on the use case "Comparative analysis of different methodologies and datasets for Energy Performance Labelling of buildings".

Objectives

  • To make a comparative analysis of different methodologies for Energy Performance Labelling of buildings, applied to sample datasets of buildings of DE, NL and ES.
  • To make the results of the comparative analysis re-usable in other geographical areas (Member States) by parties aiming to assess the energy performance labels of their building stock and interested to preliminary assess costs & benefits of applying the same (or similar) methodologies based on the availability of similar datasets, with respect to those used in the comparative analysis.

Problems addressed

Buildings are responsible for approximately 40% of the primary energy consumption in Europe and there is a vital need to take actions to improve the energy efficiency of the building stock. In particular, predictions of the heat demand at building level for an entire district or city could provide valuable support to different types of stakeholders involved in the energy efficiency policy cycle. These predictions are however affected by the lack of standardized calculation methodologies and of harmonized and interoperable building data needed to perform energy simulations.

The ultimate drawback is represented by the poor comparability of the predictions, caused by different calculation methodologies, input building data of different accuracy, heterogeneous encoding of input/output data and different ways of represent/visualize output data.

Partners

  • Dutch Kadaster (NL)
  • University of Applied Sciences of Stuttgart (DE)
  • University of Applied Sciences of Saxion (NL)
  • CARTIF Foundation (ES).

Main steps

The use case has been broken down into the following steps:

  1. Definition and application of a methodology to verify the results of the simulations made to assess the energy performance labels of buildings.
  2. Simulations with SimStadt software using as input data sample datasets of 3D buildings in four test areas:
    • a portion of the city of Essen in DE,
    • a portion of the city of Zwolle in NL,
    • a portion of the city of Enschede in NL,
    • the district of Quatro de Marzo - portion of the city of Valladolid in ES.
  3. Data transformation from CityGML LOD1 and LOD2 to INSPIRE Building 3D data models and vice versa, extending INSPIRE Building 3D data model in order to include energy-related spatial objects required in the analyses.

Results

A methodology to perform energy simulations predicting energy heat demand at building level, based on the use of SimStadt software and input data consisting of CityGML 3D building data and weather data, has been applied in four test city areas in three different Member States (NL, DE and ES) and thoroughly documented.

Energy heat demand assessed for the Essen (DE) test area
Energy heat demand assessed for the Essen (DE) test area

A comparative analysis of the simulation results has been done, aiming at providing insight into the following aspects:

  • identify the main obstacles to find and pre-process the input data required by the simulations, including the need to adapt to local contexts the building physical library used by the simulation software,
  • identify the main factors influencing the accuracy of the simulation results,
  • estimate the influence of the accuracy of the CityGML LoD of the input data on the accuracy of the simulations results,
  • identify the main sources of mismatch to be considered when comparing the simulation results with real energy consumption data.

For each of the above listed aspects, the following main conclusions can be drawn:

  • Despite the availability of 3D city models as open data is increasing, information required by the energy simulations such as building age is often available only under restricted conditions.
  • In the case of the simulations for the test area of Enschede (NL) the building physic library natively present in SimStadt and related to Germany has been successfully adapted to the Dutch building typologies, proving the viability of the adaptation.
  • The preparation of the 3D building data as input data for the energy simulations require the use of software tools which in turn require skilled people.
  • A verification methodology to guide the interpretation of the results and of their differences has been introduced.
  • The improved accuracy of the simulation results depending on the better accuracy of the 3D building input data has not been demonstrated. Several comparisons between results obtained with LOD1 and LOD2 CityGML datasets have shown that there are some aspects of the building fabric which are better considered using LOD1 datasets, e.g. the reduced over-estimation of the floor area.
  • When comparing energy simulations with real energy consumption data, it is important to highlight that energy simulations do not consider user behaviours, as well as possible energy efficiency interventions made on (parts of) the simulated buildings, which instead have a strong impact on the energy consumption.
  • In any kind of comparison of energy performance of buildings in different Member States, it is much better to compare absolute values expressed in KWh/m2/y rather than comparing the labels, because the interval values the latter refer to are fixed by country-dependant national laws.
  • Despite all the simulations documented in this report have been made with the SimStadt software, in the case of Spain the simulations have been done using also another software (ENERGIS). However, assessing the dependency of the simulation results on the simulation software would require additional investigations which are out of scope of the work undertaken.

Finally, several mapping exercises between CityGML and INSPIRE data models available for 3D Buildings have been executed and documented, improving the interoperability of input and/or output datasets of the simulations.

Benefits

Notwithstanding the above listed issues, the methodology can be re-used in other geographical areas (also in other Member States) by different types of stakeholders aiming to assess the energy performance of their building stock and interested to preliminary assess costs & benefits of applying the same (or similar) methodologies based on the availability of similar datasets, with respect to those used in the comparative analysis made in this use case:

  • Public Administrations involved in energy policy making at regional/local level,
  • Businesses working in the sector of energy renovation of buildings, utility companies, Energy Service Companies (ESCOs),
  • Citizens acting as building/building unit owners/tenants and/or willing to sell/buy/rent/rent out a building/building unit.

Resources

  • Final report
  • Mapping tables:
  • hale studio projects (provided as .halez files, therefore containing both source dataset and alignment):
    • transforming source CityGML LOD1 dataset to INSPIRE BU 3D CORE data model (download)
    • transforming CityGML LOD2 source dataset to INSPIRE BU 3D CORE data model (download)
    • transforming CityGML LoD2 source dataset to INSPIRE BU 3D EXTENDED data model (download)
  • Datasets:
    • CityGML LoD1 source dataset (download)
    • INSPIRE BU 3D CORE harmonised dataset (gml) (download)