Presentation 4 Session 1 “OPTEEMAL: enhancing BIM models with semantic web technologies to plan building retrofitting at district scale”

1. OptEEMAL: Enhancing BIM models with
Semantic Web technologies to plan
building retrofitting at district scale
Gonçal Costa
ARC Engineering and Architecture La Salle
Barcelona, SPAIN

2. Project Overview
Project founded under the work programme:
HORIZON 2020-WORK PROGRAMME 2014-2015
Leadership in enabling and industrial technologies
H2020-EeB-2014-2015 / H2020-EeB-2015
Topic: Innovative design tools for refurbishment at building and district level
(EeB-05-2015)
Participants: 13 Partners
4 RTO, 2 Universities, 2 IND, 3 SME and 2 Cities
Contact: contact@opteemal.eu
Optimised Energy Efficient Design
Platform for Refurbishment at
District Level

3. Research topic
• How to provide optimized building-district retrofitting designs
through a consistent integration of semantic representation of
the data from multiples sources: IFC models, CityGML models,
and contextual data (project, weather, occupancy, etc.).
Application of the research: the OptEEmAL platform
• Optimised Energy Efficient Design Platform for Refurbishment
at District Level
Three main components of the platform:
• District Data Model (DDM)
• Energy Conservation Measures Catalogue (ECMs)
• Automated generation of input data for simulation tools.
Project Overview: Context

4. Project Overview: What is a district?
1. Target buildings that will be refurbished

5. Project Overview: What is a district?
1. Target buildings that will be refurbished
2. Surrounding buildings that interact with target buildings (shadows)

6. Project Overview: What is a district?
1. Target buildings that will be refurbished
2. Surrounding buildings that interact with target buildings (shadows)
3. District active systems connected to the buildings (district heating)

7. Project Overview: What is a district?
• Weather data
• Energy prices
• Socio-economic data
1. Target buildings that will be refurbished
2. Surrounding buildings that interact with target buildings (shadows)
3. District active systems connected to the buildings (district heating)
4. Contextual data (weather, energy prices…)

8. Data integration perspective
Input Data District
Integration
Input Data
BIM models
(IFC standard)
GIS models
(CityGML standard)
Contextual Data
(multiple sources)
1
2
3
• Weather data
• Energy prices
• Users’ objectives
• Socio-economic data
Measures:
• Passive
• Active
• Renewables
• Control
Indicators:
• Energy
• Comfort
• Environmental
• Economic
• Social…

9. Data integration perspective
BIM models
(IFC standard)
GIS models
(CityGML standard)
Contextual Data
(multiple sources)
1
2
3
Input Data District Model Simulation
• Weather data
• Energy prices
• Users’ objectives
• Socio-economic data
Energy
Economic
Semantic
Data Models
Urban
Social
Simulation
tools
HVAC tool
ECO tool

10. Data integration perspective
Energy
Economic
Semantic
Data Models
Urban
Social
CityGML
Shadows
Validation
and Checking
Contextual data
Simulation
tools
HVAC tool
ECO tool
IFCIFCIFC
Validation
and Checking
Building models
Input Data District Model Simulation

11. Data integration perspective
Simulation
tools
HVAC tool
ECO tool
Simulation District Retrofitting

12. Data integration perspective
Simulation
tools
ENERGY DPI’s
COMFORT DPI’s
ENVIRONMENTAL DPI’s
ECONOMIC DPI’s
SOCIAL DPI’s
URBAN DPI’s
GLOBAL DPI’s
District Performance Indicators
Calculation sequence
1
Energy demand
Simulation District Retrofitting

13. Data integration perspective
Simulation
tools
ENERGY DPI’s
COMFORT DPI’s
ENVIRONMENTAL DPI’s
ECONOMIC DPI’s
SOCIAL DPI’s
URBAN DPI’s
GLOBAL DPI’s
District Performance Indicators
Calculation sequence
HVAC tool
1
2 Energy consumption
Local thermal confort
Simulation District Retrofitting

14. Data integration perspective
Simulation
tools
ENERGY DPI’s
COMFORT DPI’s
ENVIRONMENTAL DPI’s
ECONOMIC DPI’s
SOCIAL DPI’s
URBAN DPI’s
GLOBAL DPI’s
District Performance Indicators
Calculation sequence
HVAC tool
1
3
2
Global Warming Potential
Simulation District Retrofitting

15. Data integration perspective
Simulation
tools
ENERGY DPI’s
COMFORT DPI’s
ENVIRONMENTAL DPI’s
ECONOMIC DPI’s
SOCIAL DPI’s
URBAN DPI’s
GLOBAL DPI’s
District Performance Indicators
Calculation sequence
HVAC tool
ECO tool
1
3
4
2
Operational energy cost
Simulation District Retrofitting

16. Data integration perspective
ENERGY DPI’s
COMFORT DPI’s
ENVIRONMENTAL DPI’s
ECONOMIC DPI’s
SOCIAL DPI’s
URBAN DPI’s
GLOBAL DPI’s
District Performance
Indicators
Current status
of the district
Simulation
tools
HVAC tool
ECO tool
Simulation District Retrofitting

17. Data integration perspective
Optimiser
Urban
Urban
Energy
Economic
Semantic
Data Models
Urban
Social
Energy
Conservation
Measures
Catalogue
Current status
of the district
District Retrofitting District Model
Indicators

18. Data integration perspective
Optimiser
Urban
Urban
Energy
Economic
Semantic
Data Models
Urban
Social
Energy
Conservation
Measures
Catalogue
atus
rict
ng District Model Simulation
Simulation
tools
HVAC tool
ECO tool
dicators

19. Data integration perspective
Optimization DPIs
Simulation
Energy
Economic
Urban
Social
BIM models
(IFC standard)
GIS models
(CityGML standard)
Contextual Data
(multiple sources)
1
2
3
• Weather data
• Energy prices
• Users’ objectives
• Socio-economic data
HVAC tool
ECO tool
Summary

20. References
Beetz, J., Van Leeuwen, J., & De Vries, B. 2009. IfcOWL: A case of
transforming EXPRESS schemas into ontologies. Artificial Intelligence for
Engineering Design, Analysis and Manufacturing, 23(01), 89-101.
Bonduel, M., Oraskari, J., Pauwels, P., Vergauwen, M., & Klein, R. (2018). The
IFC to Linked Building Data Converter-Current Status. In 6th Linked Data in
Architecture and Construction Workshop (Vol. 2159, pp. 34-43).
BuildingSMART. (2015). IFC4 Release Summary. http://www.buildingsmart-
tech.org/specifications/ifcreleases/ifc4-release
Costa, G., Sicilia, A., Lilis, G. N., Rovas, D. V., & Izkara, J. (2016). A
comprehensive ontologies-based framework to support retrofitting design of
energy-efficient districts. Ework and Ebusiness in Architecture, Engineering
and Construction; Christodoulou, S., Scherer, R., Eds, 673-681.
Costa, G., Sicilia, Á. (2017). Methodology for data integration using SPARQL
Constructs in the AEC industry. In Proceedings of the 5th Linked Data in
Architecture and Construction Workshop (LDAC2017), Dijon, France, 13–15.

21. References
O'Donnell, J., See, R., Rose, C., Maile, T., Bazjanac, V., & Haves, P. (2011).
SimModel: A domain data model for whole building energy simulation.
Proceedings of Building Simulation 2011: 12th Conference of International
Building Performance Simulation Association, Sydney.
Pauwels, P., Terkaj, W. (2016). EXPRESS to OWL for construction industry:
Towards a recommendable and usable ifcOWL ontology. Automation in
Construction 63, 100–133. https://doi.org/10.1016/j.autcon.2015.12.003.
Sicilia, Á., & Costa, G. (2017). Energy-Related Data Integration Using
Semantic Data Models for Energy Efficient Retrofitting Projects. In
Multidisciplinary Digital Publishing Institute Proceedings (Vol. 1, No. 7, p.
1099).

22. Acknowledgements
OptEEmAL (“Optimised Energy Efficient Design Platform for
Refurbishment at District Level”) has been carried out with
the support of the European Union Horizon 2020 Framework
Programme (H2020/2014-2020) under grant agreement
n° 680676.

23. https://timepac2019.blogspot.com
If you would like to have more information
about this presentation, please contact
goncal.costa@salle.url.edu

24. Building data extraction
(Processes, issues, decisions)

25. Building data extraction
Light, Occupancy
and Equipment
Schedules
Material / Thermal
Properties
BIM model

26. Building data extraction
Lilis, G. N., Giannakis, G., Katsigarakis, K., and Rovas, D., District-aware Building Energy
Performance simulation model generation from GIS and BIM data, 4th IBPSA-England
Conference on Building Simulation and Optimization, Cambridge, UK, 2018, pp. 177-184.
2nd Level Space
Boundaries
Slab partitioning
IFC model

27. BIM modelling
and IFC export
Guidelines
IFC
Building data extraction