Modelling Energy Data in Urban Environments

MULTIPLE REPRESENTATIONS Barcelona, 11-12 April 2013

Álvaro Sicilia

ARC Enginyeria i Arquitectura La Salle

Universitat Ramon Llull, Barcelona

CONTENTS

The objective of SEMANCO is to provide methods and tools, based on semantic

modelling of energy information, to help different stakeholders involved in urban

planning to make informed decisions about how to reduce CO2 emissions in cities

by:

• Supporting access to and analysis of distributed and heterogeneous

sources of energy related data

• Modelling energy data according to standards of the Semantic Web

• Providing integrated tools that access and update the semantically modeled

data

Building repositories

Energy data

Environmental data

Economic data

Enabling scenarios for stakeholders

Building stock energy modelling

tool

Advanced energy information

analysis tools

Interactive design tool

Energy simulation and trade-off tool

Policy Makers Citizens Designers/Engineers Building Managers Planners

Regulations Urban Developments Building Operations Planning strategies

Technological

Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF)

CO2 emissions reduction!

Application domains

Stakeholders

ENERGY DATA MODELLING

3. Formal-computable

Integrated information formalized to be processed by humans and computers

2. Informal - integrated

Integrated information which can be processed only by humans.

1. Informal – dispersed

Dispersed information which can be processed only by humans.

Energy data modelling as a process of conceptualization, formalization and codification

ENERGY DATA MODELLING

3. Formal-computable

Integrated information formalized to be processed by humans and computers

2. Informal - integrated

Integrated information which can be processed only by humans.

1. Informal – dispersed

Dispersed information which can be processed only by humans.

Ontology

Data sources

Standards & references

Use cases

Data sources integration

Standard Tables

Data sources Mapping

Tables

Energy data modelling as a process of conceptualization, formalization and codification

Urban planners

Analysis and visualization Tools/Services

Use cases: Run energy performance analysis

- To define the energy performance baseline of a City, Neighborhood, and Buildings.

- To assess energy impact on new interventions (e.g. building refurbishment, new planning, new policies…)

Tools requirements Data sources needed

International Standards & References

- International technical standards (e.g. EN ISO 13786 , EN 15193 , EN 15251, NREL/TP-550-38600, …)

- Energy data modelling references (e.g. Tabula, Datamine, …)

Terminology

Energy data sources

- GIS (e.g. Terrain images, 3D building models, Land registry, …)

- Year of construction-based typologies (e.g. energy consumption, socio-economic, envelop properties, HVAC system, …)

- Climate (e.g. temperature, solar radiance, …)

Terminology Data sources

INFORMAL – DISPERSED

Energy data informally expressed and dispersed in different places and formats

USE CASES METHODOLOGY

Acronym UC10

Goal To calculate the energy consumption, CO2 emissions, costs and /or socio-economic

benefits of an urban plan for a new or existing development.

Super-use

case

None

Sub-use case UC9

Work process Planning

Users Municipal technical planners

Public companies providing social housing providers

Policy Makers

Actors Neighbour’s association or individual neighbours: this goal is important for them to know

the environmental and socio-economic implications of the different possibilities in the

district or environment, mainly in refurbishment projects.

Mayor and municipal councillors: In order to evaluate CO2 emissions impact of different

local regulations or taxes

Related

national/local

policy

framework

Sustainable energy action plan (Covenant of Mayors)

Local urban regulations (PGOUM, PERI, PE in Spain)

Technical code of edification and national energy code (CTE, Calener in Spain)

Activities A1.- Define different alternatives for urban planning and local regulations

A2.- Define systems and occupation (socio-economic) parameters for each alternative

A3. Determine the characteristics of the urban environment

A4. Determine the architectural characteristics of the buildings in the urban plans

A5. Model or measure the energy performance of the neighbourhood

A6. Calculate CO2 emissions and energy savings for each proposed intervention

A7. Calculate investment and maintenance costs for each proposed intervention

Use cases and ACTIVITIES are connected creating a tree

A USE CASE specification template

STANDARD TABLES - 24 categories including building use, climate, territory, socio-economic, and building geometry.

- Each category contains terms and their relations (aggregation, subsumption)

- Each term is referred to a specific Standard (EN 15603, TABULA,…), is typed (String, integer,…), and if it is applicable is measured (square meters, CO2 tons per year…)

Name/Acronym Description Reference Type of data Unit

Building

construction as a whole, including its

envelope and all technical building

systems, for which energy is used to

condition the indoor climate, to provide

domestic hot water and illumination and

other services related to the use of the

building

EN 15603 - -

has Building_Name name (ID) of the building - string -

has Age construction period of the building - string -

is Year_Of_Construction year of construction of the building - string -

is Age_Class

period of years to be defined according to

typical construction or building properties

(materials, construction principles, building

shape, ...)

TABULA string -

has From_Year first year of the age class TABULA string -

has To_Year last year of the age class TABULA string -

has Allocation specification of the region the age class is

defined for TABULA string -

has Identifier - SUMO A,B,C,D -

has Address address of the building - string -

has First_Part_Of_Postcode first part of the postcode of the building

location SAP string -

has Building_Typology building typology - string -

is Flat apartment in a building - string -

is Detached_Building small building, without attached buildings TABULA string -

is Semi-Detached_Building small building, with an attached building TABULA string -

Standard tables collect and classifies the information and knowledge from different sources: Use cases, Standards and data.

DATA SOURCES MAPPING TABLES

Data source Data name (in the Data

source)

Data name (according to

standard tables)

Data category

Tb_PercentageWindowArea-AgeConstruction Percentage_Windows_Area Percentage_Windows_Area Not classified data

Tb_WindowParameters-YearConstruction Window_U-value Window_U-value Building technical data

Tb_WindowParameters-YearConstruction Window_Glass_g-value Window_Glass_g-value Building technical data

Tb_RoofUValue-YearConstruction Roof_U-value Roof_U-value Building technical data

Tb_SkylightParameters-YearConstruction Skylight_U-Value Skylight_U-Value Building technical data

Tb_SkylightParameters-YearConstruction Skylight_Glass_g-value Skylight_Glass_g-value Building technical data

Tb_Manresa_Climate Global_Solar_Irradiance Global_Solar_Irradiance Climatic data

Tb_Manresa_Climate Air_Temperature_Maximum Air_Temperature_Maximum Not classified data

Tb_Manresa_Climate Air_Temperature_Minimum Air_Temperature_Minimum Not classified data

… … … …

Data source mapping tables maps the data sources (e.g. Database) structure (table and columns) to the Standard Tables previously developed

INFORMAL SHARED VOCABULARY

ONTOLOGY

Codification of the Standard Tables into an Ontology

- It is coded in OWL language (it can be seen as a XML file which can be processed by computers) - An Ontology is composed of two types of hierarchies:

a) Subsumption (Taxonomy) b) Aggregation (Properties)

- We have created an ontology editor which hide the complexity of ontology editing process. - 868 Concepts, 405 relations, and 278 properties

a) Subsumption hierarchy

b) Aggregation hierarchy

FORMAL SHARED VOCABULARY

ONTOLOGY EDITOR

DATA SOURCES INTEGRATION

Codification of the Standard Tables into an Ontology

Data source

SQL-SPARQL Rewritter

Urban planners

Analysis and visualization Tools/Services

SPARQL

RDF

Ontology Mapping

Collaborative Web

Environment

DATA SOURCES INTEGRATION

Ontology Mapping Collaborative Web Environment

TWO EXAMPLES

Get U value of a wall of building typologies:

Get U value of a roof of building typologies

CONCLUSIONS

• We have implemented a set of procedures, templates, methods, tools to conceptualize energy data in urban planning.

• The energy related data –use cases, standards, data sources– have been represented in different ways from informally to formal format enabling their processing by computers.

• An ontology including more than 800 concepts has been created modelling the energy-related data in the urban planning domain.

• This way, different data sources from different domains could be integrated and could be accessed using the same terminology.

SUMMARY

CONCLUSIONS

SEMANCO is being carried out with the support of the European Union’s FP7 Programme “ICT for Energy Systems” 2011-2014, under the grant agreement number 287534 .

If you would like more information, please contact us

sicilia@salleurl.edu

or visit our web site

www.semanco-project.eu