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DEMAND CHARACTERSATION BY INSPIRE BASED EPC GENERATION FOR ENERGY URBAN PLANNING TOWARDS THE DECARBONISATION OF BUILDINGS AND DISTRICTSGEMA HERNÁNDEZ MORAL – FUNDACIÓN CARTIFCÉSAR VALMASEDA TRANQUE – FUNDACIÓN CARTIFGIACOMO MARTIRANO – EPSILON ITALIA
VICTOR I. SERNA – FUNDACIÓN CARTIFGIULIA MASSA– FUNDACIÓN CARTIF
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
Non profit privateorganisation with a finalgoal: innovation
Foundation Centro Tecnológico CARTIFParque Tecnológico de Boecillo, 20547151 Boecillo, ValladolidSPAINhttp://www.cartif.com/
Research CentreGenerate technologicalknowledge to betransferred to companies /businesses to improve theircompetitiveness
Energy Division
Energy PoliciesDigital and industrial systems
Agroalimentationand processes
Smart Cities
Energy Efficiency
Renewable energy
Smart Grids
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
1. Context: legislative aspects to bear in mind2. ELISE (Energy Pilot) / PLANNER project3. Use Case 1: INSPIRE Harmonisation of EPC datasets4. Spanish Case study: replication of the results5. Conclusions
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
Before delving into the topic…
ENERGY PILOT(ELISE Action)
PLANNER PROJECT(Internal Project)
1. Context: Legislative aspects to bear in mind (EPBD, CoM, EED)1. Context: Legislative aspects to bear in mind (EPBD, CoM, EED)
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
- EPBD: Energy Performance of Buldings Directive
- CoM: Covenant of Mayors
- EED: Energy EfficiencyDirective
Image source: http://publications.jrc.ec.europa.eu/repository/handle/JRC104587
- INSPIRE Directive: locationdata
2. ELISE (Energy Pilot) vs PLANNER2. ELISE (Energy Pilot) vs PLANNER
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
UC1: INSPIRE Harmonization of existing Energy Performance Certificate datasets and creation of a web application
UC2: Benchmark of different Energy Performance Labelling of buildings
UC3: Assessing the Energy Performance of buildings with dynamic measured data
UC4: Supporting Energy Efficiency driven renovation planning of the building stock at local level
UC5: Supporting integrated energy planning and monitoring at urban/local level (SEAP BEI/MEI)
UC6: Supporting the design and implementation of a regional energy strategy
MAIN FOCUSPLANNER (internal CARTIF project)
Input data (cadastre) EPCs calculation
Output (energy demand
)MAPP
ING
Input data
(EPCs)
MAPP
ING
geo-rEPCs
UC1
UC2
VA
LID
ATI
ON
PLA
NN
ING
UC3 UC4
Source: PLANNER project, CARTIF, 2017
Energy Pilot (JRC)
3. Use Case 1: INSPIRE Harmonisation of EPC datasets3. Use Case 1: INSPIRE Harmonisation of EPC datasets
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
Objectives
Achievements
Replication of results
To establish an accessible and interoperable common knowledge base for EPC datasets to support local government and private sector involved in energy efficiency policies.
- Study of Trento’s EPC dataset structure (based on real EPCs)- Target data model proposal following INSPIRE- Creation of a web app to access EPCs following INSPIRE specifications
Spanish case study: EPC harmonisation based on the target model
3. Use Case 1: INSPIRE Harmonisation of EPC datasets3. Use Case 1: INSPIRE Harmonisation of EPC datasets
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
Image source: http://inspire-sandbox.jrc.ec.europa.eu/energy-pilot/use-case-1/data-models/uml-html-viewer/
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
Methodology followed
1 EPC submission context in Spain
2 Datasets contained in an EPC
3 Comparison with target data model
CadasterPLANNER energy
demand data
PLANNER data model
EPC national data model(national xml)
1. Context1. Context 4. ES case study4. ES case study
REGIONS (e.g.CYL) monitor and validate results of certificates + show to public
EPC REGISTER
PUBLICLY AVAILABLE DATA
In each region (in Spain)
Maintained by each region
Different amount of information provided depending on the region
REG
IONS
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
1: EPC submission context in SpainEPBD
(Energy Performance of Buildings Directive)
Need to generate EPCs
Need a calculation methodology
Establish “Common general framework for the calculation
of energy performance of buildings”
Base it on standard:CEN EN 15603
(more standards – CENSE)
EURO
PE
MEMBER STATES (SPAIN) transpose the need to generate certificates and general framework
FOUR VALIDATED EPC TOOLS
ALTERNATIVE PROCEDURES CE3X
CERMA
CEX
HULC
Justification required
SPAI
N ISSUE EPC
energy label
+
PDF format
XML formatAt
national level!
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
2: Datasets contained in a Spanish EPCPDF format XML format
EPC PDF format - Spain Sample of document – XML EPC content
Semantic richness of Spanish PDF / XML EPC version
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
3: Comparison with target data model
Building
Abstract Building
Professional
Energy System
Energy Conversion System
Certificate
Abstract Construction
Certifier
TARGET MODEL
Datos del certificador
Identificación edificio
Datos generales y geometría
Datos envolvente térmica
Instalaciones térmicas
Instalaciones iluminación
Condiciones generales funcionamiento y ocupación
Energías renovables
Demanda
Consumo
Emisiones CO2
Calificación
Medidas de mejora
Pruebas, comprobaciones e inspecciones
Datos personalizados
SPANISH EPC MODEL (XML)
Edificio
Certificato
Generatori
Impianti
Certificatore
TRENTO EPC MODEL
Main categories in each data model
1. Main category analysis
2. Attribute per attribute comparison (considering multiplicity)
3. Conclusions /quantification
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
3: Comparison with target data model
1. Main category analysis
2. Attribute per attribute comparison (considering multiplicity)
3. Conclusions /quantification
Trento – Target data model tables(UC1 work)
Target data model – UML version(UC1 work)
Target data model – analysis tables
STEP 1
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
3: Comparison with target data model
1. Main category analysis
2. Attribute per attribute comparison (considering multiplicity)
3. Conclusions /quantification
Spanish data model – analysis tables
STEP 2
Spanish XML official version
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
3: Comparison with target data model
1. Main category analysis
2. Attribute per attribute comparison (considering multiplicity)
3. Conclusions /quantification
Spanish data model – analysis tables
STEP 3
Target data model – analysis tables
CONCLUSIONS / QUANTIFICATION
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
3: Comparison with target data model
1. Main category analysis
2. Attribute per attribute comparison (considering multiplicity)
3. Conclusions /quantification
Concept Number of elements
(Target model)
Number of elements
(Spanish XML model)
Total number of attributes1 113 254
Main categories (feature types)3 10 17
Sub-categories (data types)4 14 50
Mandatory attributes2 24 (21.23%) 67 (26.38%)
Optional attributes2 89 (78.76%) 187 (73.62%)
Codelists5 24 42
General quantification of the data models
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
Concept Number of elements
(Target model)
Number of elements
(Spanish XML model)
Matching attributes 13
One to more relationships 21 33 (+7)
Similar attributes 9
Other sources 43 -
Non-matching attributes 33 199
Total number of attributes 119 254
3: Comparison with target data model
1. Main category analysis
2. Attribute per attribute comparison (considering multiplicity)
3. Conclusions /quantification
Differences found in the comparison
4. Spanish Case study: replication of the results4. Spanish Case study: replication of the results
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
3: Comparison with target data model
1. Main category analysis
2. Attribute per attribute comparison (considering multiplicity)
3. Conclusions /quantification
• Perfect matching was difficult to obtain: different definitions and some attributes sometimes corresponded to more than one attribute in the opposite data model
• Mismatches in terminology: same concepts were treated differently in both data models
• Non-matching attributes: in Spanish data model are related mainly to building envelope data, improvement measures and energy labelling data (demand, consumption, emissions…)
It is important to highlight:
• Input and Output data: while the target data model deals with mainly output data and some necessary input data to calculate the EPC, the Spanish data model is semantically rich enough to hold all necessary input data.
5. Conclusions5. Conclusions
1. Context1. Context 2.ELISE vs PLANNER2.ELISE vs PLANNER 3. Use Case 13. Use Case 1 4. ES case study4. ES case study 5. Conclusions5. Conclusions
Demand characterisation by INSPIRE based EPC generation for energy urban planning towards the decarbonisation of buildings and districts
• EPC role not to be sub-estimated (not to be an administrative burden)
• INSPIRE role is highly relevant - in supporting, harmonising, providing the attributes across all member states and making them accessible through catalogues. In addition, location information allows to analyse patterns by location, be able to implement action plans / policies
• Replication possibilities: While the replication possibilities are necessary and possible, there are many differences related to the way EPCs are defined /conceived in each Member State and how the EPCs are managed (registers etc).
• Different conceptualisation of EPCs resides in calculation methodology adopted / interpretation of EPBD.
• Common calculation methodology in member states and registers at national level following the same data model would be highly beneficial to support energy action plans etc. Assuring that the same input data (harmonised across Europe and terminology) is deployed combined with the same calculation methodology will ensure reliable and comparable results across member states.
• Moreover, assuring an adequate methodology and making these data available to the public might also contribute for a better uptake of the EPCs .
Any questions?
THANKS FOR YOUR ATTENTION!http://api.voxel3d.es/examples/ENERGIS/portal/
Gema Hernández Moral - Fundación [email protected]
Víctor Iván Serna – F. [email protected]
Giulia Massa – F. [email protected]
César Valmaseda – F. [email protected]
Giacomo Martirano – Epsilon [email protected]