Assessing energy performance of buildings using modeling based on existing administrative and topographical data

Transcription

1 Assessing energy performance of buildings using modeling based on existing administrative and topographical data

2 Agenda Introduction and goal Methodologies for determining Energy Performance of buildings Datasets used for the use case Results Conclusion and discussion 2 Introduction

3 Introduction The work presented is being executed in the frame of one of the use cases of the Energy Pilot, which is a pilot project of the ELISE Action (European Location Interoperability Solutions for e-government) of the ISA2 Programme (Interoperability solutions for public administrations, businesses and citizens) of DG Informatics of EC, running from 2016 to Partners involved: Dutch Kadaster (NL) Stuttgart Technology University of Applied Sciences HFT (DE) Saxion University of Applied Sciences (NL) DG JRC of European Commission 3 Introduction

4 Goal A comparative analysis of different methodologies for Energy Performance Labelling of buildings, applied to sample datasets of buildings in NL and DE. Re-usability of results in other geographical areas (Member States) by organizations that want to assess the energy performance labels of their building stock To provide parties that assess energy performance of buildings a preliminary costs & benefits analysis for applying methodologies and datasets used in this comparative analysis. 4 Introduction

5 Methodology for The Dutch Energy Performance Label provisional energylabel was issued to every home-owner ( ) based on a simplified model and data from Key Registers Mainly used in a communication campaign Definitive EPBD compliant label is mandatory when selling a house, enforced with a fine Based on an advanced model and dwelling specific data A webtool is used for application, data input, and expert validation of the energy label No on site inspection is needed Key registers and other national datasets Key Register for Addresses & Attribute Provisional label Registration definitive label Enforcement transactions without label Buildings (BAG) Building use x Adress x Year of construction x x Dwelling type (derived attribute) x x Key Register Cadastre (BRK) Transactions x Characteristics seller property x Characteristics owner property x Key Register for Large Scale Topography Geometry National energy label database Energy label (EPC) x Additional dwelling specific various, including implemented energy information reducing measures x Aerial imagery 5

6 EPBD compliant certificate in Germany Certificate (EnEV) will be issued based on heat demand or on heat consumption Certificate is mandatory for new buildings (based on demand) buildings to be sold non-residential buildings Residential buildings 1977 or older based on demand (since 2008) (with exceptions) Non-residential based on demand or consumption 6 Introduction

7 Research approach Simulate building heating demand based on 3D city model using monthly energy balance (DIN 18599) Pre-Calculate demand oriented energy performance label 7 Introduction

8 Building Nr An[m2] A/V Ht' [W/m².K] Q[kWh/a] Building Nr Building Nr Non An[m2] An[m2] Non A/V A/V Non Ht' [W/m².K] Ht' [W/m².K] Non Q[kWh/a] Q[kWh/a] Non Building Type Residential Mixed Residential Residential Mixed Residential Residential Mixed Residential Residential Mixed Residential Residential Mixed Residential EFH_B ,608 0,901 1,431 1, EFH_C ,877 1, EFH_H ,883 0, EFH_I ,604 0, EFH_J ,818 0, GMH_B ,405 0,413 0,430 1,926 1,958 1, GMH_C ,376 0,467 1,780 1, GMH_D ,364 0,337 1,628 1, GMH_E ,350 1, MFH_B ,430 0,479 0,468 1,513 1,549 1, MFH_C ,404 0,470 0,420 1,549 1,536 1, MFH_D ,415 0,458 0,582 1,495 1,430 1, MFH_E ,449 1, MFH_F ,497 1, MFH_G ,429 0,499 0,975 0, MFH_H ,438 0,350 0,397 0,865 0,840 0, MFH_I ,395 0,403 0,554 0,495 0,492 0, MFH_J ,493 0,421 0,424 0, RH_B ,518 0,495 0,810 1,389 1,450 1, RH_D ,635 1, RH_H ,812 0,487 0,860 0, RH_I ,566 0, RH_J ,541 0, , SimStadt Model Gebäudesimulation 3D City model Building Attributes 3D-spatial-data server German Buidling Typology (IWU) # Building Physics # Building usage # Refurbishment scenario Building Libraries Quality check tool SimStadt Analysis Simulation Irradionce, tambient & sky temperature Simulation Model 2D Map 3D Viewer Export Outputs TOTAL , , ,8 0,533 0,474 0,527 1,107 1,320 1,

9 3D City Model LOD1 City GML format Simstadt is suitable for all LoD-Stages (1-4) Includes attributes concerning the building year and function of the buildings LOD2 LOD3 LOD4 9

10 3D-Stadtmodell Europe INSPIRE BU 3Dl 3D City Models Germany Report CityGML ADV profile State (ADV) CityGML ADV profile Exchange Municipality CityGML SIG3D Modelling Guide

11 Available data in Netherlands (1) Provisional energy label: simplified model and data from public Key Registers Dwelling type untill Year of construction (period) and later Seperate Semi-detached Detached corner Detached Flat / appartment G F D C C B B B A A G F D C C C B B A A G F D C C C B B A A F E C C C C B A A A G E E B C C C B A A Source: 60 Referenties ten behoeve van voorlopige energielabel v2.0 oktober 2014, RVO 2014 Data Dwelling type Year of construction Energy label Provisonal X X 11

12 Available data in Netherlands (2) Data used for implementing Energy Performance Labelling for dwellings in the use case city of Zwolle Key registers and other national datasets Key Register for Addresses & Buildings (BAG) Attribute Building use Adress Year of construction Dwelling type (derived attribute) Key Register Cadastre (BRK) Transactions Characteristics seller property Characteristics owner property Key Register for Large Scale Topography Geometry National energy label database Energy label (EPC) various, including Additional dwelling specific implemented energy information reducing measures Aerial imagery 3D-availability Netherlands: Countrywide: 3D TOP10NL (2015), Level of Detail 1 (LOD1) Pilot area Zwolle: 3D BGT (2017), LOD1, Interoperable with Key Registers Many local initiatives on municipal and district scale, LOD1 LOD4

13 Available data in Germany LoD 1 and LoD 2 models available from most states Some open data (NRW 10 Million Buildings) Usually Missing: Year of construction (third party data source) Refurbishment (not available at all) 13 INSPIRE E k h INSPIRE C f 2018 A t (BE) 20 S t b

14 Comparing methodologies and data Essen (DE) Zwolle (NL) current use case 2 current use case 2 Methodology Certificate SimStadt model RVO Energy Label model SimStadt model Expressed as kw/m2/yr kw/m2/yr Label character (A tm G) or energy index kw/m2/yr Available administrative data Third party Third party Key Register (BAG) Key Register (BAG) Available geometrical data LOD2 LOD2 3D BGT (LOD1) 3D BGT (LOD1) 14 INSPIREd Energy workshop INSPIRE Conference 2017, Kehl (DE), 5 September 2017

15 IV. Preparation of the Zwolle data CityGML - must be changed for Simstadt Provided by Dutch cadastral office Includes water bodies, 3D surface model, buildings, traffic areas, planted areas, bridges In Total 1923 buildings included Only buildings required for Simstadt B1

16 Results of comparative analysis of different methodologies for Energy Performance Labelling of buildings 1. For the Dutch city of Zwolle, Dutch preliminary energy performance certificates were compared to energy performance certificates derived from the Simstadt model. 2. An additional analysis of the German city of Essen included actual energy consumption data. Simstadt outcomes for two 3D models with different levels of detail (LOD1 and LOD2) were compared.

17 1. Results comparing Dutch energy performance certificates to energy performance certificates derived from the Simstadt model Labels Zwolle issued by RVO Simstadt Simulation results B1

18 1. Results comparing Dutch energy performance certificates to energy performance certificates derived from the Simstadt model Both label assignments indicate that most of the buildings have poor heating demand balance Number of labels issued by RVO Number of labels calculated by Simstadt A B C D E F G A A+ B C D E F G H B1

19 2. Results comparing Simstadt outcomes for two 3D models with different levels of detail (LOD1 and LOD2) and real consumption data for city of Essen Comparing labels LOD1 and LOD2 (number) Comparing labels LOD1 and LOD2 (share) % 2% 20% 10% 21% 19% 29% 17% 15% 14% 10% 22% 8% 5% 6% 0% A B C D E F G H A B C D E F G H Simstadt LOD1 Simstadt LOD2 Simstadt LOD1 Simstadt LOD2 In total there are 296 identical buildings in the datasets LOD1 and LOD2 B1

20 2. Results comparing Simstadt outcomes for two 3D models with different levels of detail (LOD1 and LOD2) and real consumption data for city of Essen The deviation between LOD1 and LOD2 heat demand modelling is on average 20 kwh/m³.yr (based on 148 buildings) B2

21 2. Results comparing Simstadt outcomes for two 3D models with different levels of detail (LOD1 and LOD2) and real consumption data for city of Essen Comparing label calculated via real consumption data and Simstadt LOD1 and LOD Some data uncertainties: Information on renovations was unavailable Information on heated basements/attics was unavailable real consumption data for only 148 identical buildings in each of the three datasets A+ A+ B C D E F G H Energy consumption LOD1 LOD2

22 Results and conclusions 1. Conclusions comparing Dutch energy performance certificates to energy performance certificates derived from the Simstadt model Both methodologies find the same general direction of results But comparing between models is difficult different scales used (Simstandt 9 labels, RVO 7 labels) data uncertainties Greater data-harmonization is important Including more building specific information (i.e. energy reduction measures taken) is important in future research 2. Conclusions comparing Simstadt outcomes for two 3D models with different levels of detail (LOD1 and LOD2) and real consumption data for city of Essen Relatively large differences were found between heat demand models for LOD1 and LOD2 Relatively large differences were found between actual heat demand and heat demand modeled in LOD1 and LOD2 It may be necessary to optimize the data set-specific parameters (for instance include more relevant building specific information) B2

23 Completed and planned activities Energy Pilot Completed the formal process of signing a multilateral non-monetary collaboration agreement between JRC and the partners involved Carried out a comparative analysis of the Energy Performance Labelling of buildings currently in use in NL and DE Carried out the application of the two methodologies to sample datasets of NL and DE Planned INSPIRE harmonisation of the datasets produced as output of the application of the methodologies Cost-benefit analysis Dissemination activities, including the publication of a scientific paper 23