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

Transcription

1 Comparative analysis of different methodologies and datasets for Energy Performance Labelling of buildings City of Essen (Germany) City of Zwolle (The Netherlands)

2 Agenda Introduction and goal Methodologies Datasets Challenges and results 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 To make a comparative analysis of different methodologies for Energy Performance Labelling of buildings, applied to sample datasets of buildings of DE and NL. To make the analysis results 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. 4 Introduction

5 Methodology of The Netherlands Energy Performance Label A provisional energylabel was issued to every owner of a dwelling ( ) based on a simplified model and data from Key Registers Mainly used in a communication campaign A 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 of the dwelling is needed 5

6 Average energy index Dutch dwellings per year of construction Source: Voorbeeldwoningen Onderzoeksverantwoording, AgentschapNL, Sittard, Ministerie van Binnenlandse zaken en koninkrijksrelaties. 6

7 Provisional energy label: simplified model and data from public Key Registers Model 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 7

8 Definitive (EPBD compliant) energy label: advanced model, data from public Key Registers and additional dwelling specific data Model based on: NEN 7120 NEN NEN 1068:2012 additional prescriptions ( ) Data Provisonal Energy Label Building usage X X Dwelling type X X Year of construction X X Dwelling subtype X Floor surface X X Type of window pane X Type and age of heating units X Presence of solar panel or boiler X Air ventilation system X Facade and roof insulation X EPBD compliant Energy Label 8

9 Energy index, Energy Label and Energy Usage Former Energy Label (before ) Index, Label and Usage are related in the Methodology Current Energy Label (after ) Index, Label and Usage are not related 9

10 Utilisation and significance of the EPL in The Netherlands 40% of all dwellings have an EPBD compliant label 85% of sold houses are labeled Convenant: Housing corporation dwellings have mean label B by 2020 Several banks give mortgage discounts for good labels In 2023 only C or better labeled office buildings are permitted 10

11 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 11 Introduction

12 Research approach Simulate building heating demand based on 3D city model using monthly energy balance (DIN 18599) Pre-Calculate demand oriented energy performance label (no certificate!) 12 Introduction

13 How to calculate energy consumption? (DIN V 18599): Qh heating demand Qw hot water heating demand Qs solar gains Qi internal gains HT transmission heat loss HV ventilation losses 13 Introduction

14 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,

15 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 15

16 Visualization heating demand in Essen 0/showcases/essenlive 16

17 National system of key registers in The Netherlands Building related Administrative Data Building usage Dwelling type Year of construction Floor surface Provisional Energy Label X X X X Finalised 17 Sources In progress

18 Kadaster sources BRK Key Register Cadastre BRT WOZ BAG GBA Key Register Topography oamsterdam A 1234 Key Register opurchased Addresses on 1 May, & Buildings 1999 ourban area opurchase price 240,000 o6 m NAP osurface Real Estate orabo area Valuation 150 Mortgage sq. M. oyear Municipal of omortgage construction Personal loan 1900 Records 260,000 o Database 250,000 oright of Way omonument ojan de Vries o01/06/1970 And much more... Energy labels of buildings Spatial plans Public restrictions Cable and Pipeline information Centre Public Services on the MAP 18 Sources

19 Available 3D data The 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 19

20 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) 20

21 3D City Models 3D-Stadtmodell Europe INSPIRE BU 3Dl Germany State (ADV) Exchange Municipality Report CityGML ADV profile CityGML ADV profile CityGML SIG3D Modelling Guide

22 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) 22

23 Buildings Classification NL Usage of building classification NL Housing (dwelling) Gathering Cell (prison) Health Industry Office Lodging (hotel) Education Sport Shop Other Dwelling type classification NL Separate house Semi detached house Detached house Detached corner house Flat / appartment 23

24 Building Typology DE

25 Mapping CityGML to INSPIRE BU 3D Step Identified 5: 4: 3: 2: 1: Set Map Import Default for Source Test Source Target areas to Schema Data in Essen Geometry Target Schema Successfully converted LoD 1 CityGML model to INSPIRE BU 3D using HALE Studio software LoD 2 needs to be further investigated Apply to large model Exent SimStadt to work with INSPIRE data 25

26 Planned activities Complete the formal process of signing a multilateral non-monetary collaboration agreement between JRC and the partners involved (by the end of 2017) Comparative analysis of the Energy Performance Labelling of buildings currently in use in NL and DE Application of the two methodologies to sample datasets of NL and DE 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 (by the end of 2018) 26

27 Conclusions Detailed planning of the use case activities completed Test areas in DE and NL identified Input data in test areas collected Preliminary tests on data transformation from CityGML LOD1 to INSPIRE BU 3D completed Main challenges identified Future activities planned 27

28 Thank you for your attention! Martinus Vranken: Volker Coors: Christian Struck: Giacomo Martirano: 28