This document discusses using data-based approaches for urban energy modeling. It describes the InSMART project which aims to establish a methodology for sustainable urban energy planning applied to four European cities including Nottingham. The project employs GIS databases, building energy simulations, transport modeling, and TIMES-MARKAL energy system modeling. It discusses constructing a residential building stock model for Nottingham including challenges around data gaps, quality and consistency issues in key geospatial datasets.

1. Data based approaches to urban energy modelling
Gavin Long

2. Presentation plan
• The InSMART project
• Geospatial data for urban modelling
• Constructing a residential building stock model for
Nottingham
• Data challenges for city scale micro-simulation

3. InSMART – Project overview
• A three year FP7 funded project
due to complete end of 2016.
• Aims to establish and implement a
comprehensive methodology for
enhancing sustainable urban
energy focused planning.
• Methodology applied to four
European cities (incl. Nottingham)
• Employs specialized tools and
models:
• Comprehensive GIS energy
database
• TIMES-MARKAL planning
model
• PROMITHEE Multi-criteria
decision making method
Waste & Utilities Analysis
Buildings Energy
Simulation Model
Transport & Mobility
Analysis
City Planning Platform
City GIS Energy
Database
Sustainable Energy
City Plan
Decision Makers
Stakeholders
Citizen Groups

4. InSMART – Project structure
Work Package Description Outputs/ Deliverables
WP1 Development of the cities’ GIS platform
on energy use and needs
GIS Energy database for each city
Building and transport surveys
WP2 Analysis of the cities’ building stock Residential building energy simulations
Energy use for non-residential buildings
Building energy simulation report
WP3 Transport and mobility analysis Transport surveys
Transport simulation model for each city
Transport energy simulation report
WP4 Analysis of the cities’ energy systems and
networks
City energy generation (including RES potential)
Energy associated with city services (e.g. waste
management, water, public lighting)
WP5 Integrated planning tool for the
development of strategic sustainable
energy plans
TIMES-MARKAL model for each city
Optimal energy scenarios
WP6 Development of mid-term
implementation action plans
Sustainable energy action plans to 2030 for each
city
WP7/8 Dissemination and project management Project web site

5. InSMART – WP1.2 Building Survey
• 600 surveys carried out on
residential properties using
professional energy assessors
• Survey sample population designed
to match typological distribution for
the city housing stock

6. InSMART WP2 - Analysis of city building stock
• Work divided across three main
subtasks
• Task 2.1: Identify appropriate
model resolution
• Task 2.2: Identify and
simulate the representative
building typologies
• Task 2.3: Simulate scenarios
for representative building
typologies

7. InSMART WP5 - TIMES-MARKAL modelling
• The Integrated MARKAL-EFOM System combines
advanced versions of MARKAL models.
• Linear programming based bottom-up energy
models
• Integrated modeling of the entire energy system
combining economic, environmental and energy
factors
• Typically used on a long term horizon (20-100
years) and at the national scale for a range of
energy scenarios
• Scenario based
• Model output is the optimal technology mix
selected to meet demand at minimum ‘cost’

8. GEOSPATIAL DATA FOR URBAN MODELLING
Approaches to urban modelling
Data types, aggregation units and sources

9. Approaches to urban modelling
Micro
• Micro simulation models such as CitySim. Includes detailed 3D building
models in their urban context. Models energy and mass flows between
all simulation elements are captured and explicitly simulated
Typological
• Typological samples of buildings are simulated in detail using building
energy simulations (e.g. Energy+, ESP-r).Simulation results extrapolated
to actual building stock data based on statistical data [InSMART
approach]
Macro
• Macro scale urban models. Statistical methods used to predict building
energy performance using homogeneous models of stock

10. Spatial datatypes
Vector-based (point, line, polygon) Raster-based (image, text)
H
Multi-attribute format
Data efficient representation
Highly accurate scaling
Single value (pixel/cell) format
Doesn’t require explicit geographical
coordinates
Computationally simple to analyse and process

11. Spatial aggregation units
Census Area Statistics (CAS) Wards Middle Layer Super Output Areas (MSOA)

12. Spatial aggregation units
Lower Layer Super Output Areas (LSOA) Output Areas (OA)

13. Potential data sources for urban modelling
Type Source Resolution Content
Spatial OS Mastermap 1:1250-1:10000 Building outlines, land use, road geometry, infrastructure
and Building Height Attribute (BHA) extension available
Digital Elevation
Models (DEM)
0.25-2m grid Terrain features, ground levels, building heights
Insight Mapping /
NOMAD
1:500-1:500000 Nottingham City Council’s GIS mapping portal. Integrates
OS, ONS and NCC data includes Mastermap, aerial
photography, historic mapping.
NLPG / AddressBase Property Building type, use and location. Number of properties in
multi-occupancy buildings
Building English Housing
Survey
Sample
households
Building type, construction, age, energy systems, thermal
fabric (published annually). Based on a continuous
survey of sample of UK residential building stock
UK Census LSOA/OA Building type, heating systems, tenure
Nottingham City
Homes Stock database
Property (NCH
owned only)
Building type, construction material, date of construction
InSMART Building
survey
Sample
Households
Building fabric and modifications, floorplans, energy
systems and use, occupancy profile, electrical appliances

14. Potential data sources for urban modelling
Type Source Resolution Content
Energy Neighbourhood
Statistics (DECC)
LSOA (2008) /
MSOA (2005)
Annual electricity and gas usage for all metered
domestic properties
Non domestic energy
performance register
Commercial &
public premises
Annual energy use and energy efficiency of non-
residential buildings with a floor area of
>2500sq.m based on rd-SAP calculations
Domestic energy
performance register
Residential
properties
Annual energy use and energy efficiency of
properties where an EPC survey has been
undertaken (e.g. most rented properties)
National surveys (EHS,
NEED, EFUS)
Sample
households
Average energy use statistics for typical
households based on size, type and age
Demographics
& Behaviour
UK Census LSOA (OA) Range of datasets on households including age,
size, education, vocation, etc.
MOSAIC Postcode Categorisation of households by a range of
factors (e.g. financial, educational, lifestyle and
vocational) (updated regularly)
Misc. studies Households /
Individuals
Range of surveys relating to energy related
behaviour (e.g. Time Use, Powering the Nation)

15. Key data types - OS Mastermap

16. Key data types - LLPG

17. CASE STUDY
Constructing a residential building stock model
for Nottingham

18. Creating a base building model
• Initial building attribution based on application of InSMART guidelines
with existing 2008 Cities Revealed dataset (©GeoInformation Group)
• 16 residential typologies identified based on construction period (5
categories) and building form (flat, terrace, semi-detached and
detached)

19. Limitations of the base building model

20. A modified base building model

21. Extending the base building model

22. Residential building age

23. Typological distribution of stock (ward level)
Victorian Terrace (T2) Post war terrace and semi-detached (T7)

24. Annual mean gas use by Super Output Area, 2011
Source: Domestic energy consumption figures, DECC

25. Data Challenges for city scale micro-simulation
Data gaps, quality and consistency

26. Data issues - Overview
• Errors / Inconsistencies in key
datasets
• Typological classification
errors remaining from CR data
• Incomplete property
attribution in NLPG/ABP
• Inconsistent application of
building classifications in
NLPG/ABP
• BHA coverage and quality
• Multi-occupancy and mixed
use buildings
• Multi-polygon buildings
• Buildings without NLPG/ABP
data point entry.

27. Example - Modelling multi-polygon buildings

28. Example – Building height data quality issues

29. Thank you for your time (and patience)
Questions?