On the simulation of energy use in buildings in their urban context...

Darren Robinson Sheffield School of Architecture (80%) + University of Nottingham Faculty of Engineering (20%)

The broader urban context…

The environmental unsustainability continuum

Autarky

Autonomy Regional autonomy

Industrialised city

(non-renewable resources) (non-renewable resources)

(renewable resources)

(internal entropy growth)

Cities are: Self-organising, non-linear and open.

Project objectives: •  To understand the complex interrelated and

competing factors influencing urban sustainability. •  To holistically define, measure and model it. •  To identify pathways to transition developed cities

and accommodate growth in developing cities in near-sustainable ways.

•  To define policy and governance structures to implement these pathways in practice.

Sustaining Urban Habitats: An interdisciplinary approach

Grant: Research Programme Grant, 2014 Funding: £1.75M (£3.4M total: >70PYs [7RFs, 18PhDs]) Duration: 5 years (Feb 2015 – Jan 2020)

Given some objective function characterising environmental unsustainability, how should our (hypothetical) city be configured to minimise it?: •  How dense or compact? •  How diverse: entropy minimising? •  Which transport modes and technologies? •  Which industries and how tightly coupled?

Some key questions…

•  How should buildings be designed to reduce resource demands and of which materials should they be built?

•  To what extent can behaviour reduce demands? •  Which (thermal and electrical) energy conversion,

storage, distribution and control technologies? •  Which water treatment / management strategies? •  How autonomous can food production be? •  …etc •  An integrated urban model should be capable of

responding to all these questions, and more…

4) Describe HVAC and ECS systems

5) Simulate and analyse

1)  Create or import 3D model and its clones

2)  Describe envelope composition

3)  Describe occupancy and appliance schedules

Microsimulation: CitySim

Robinson, Comnputer modelling for sustainable urban design, Taylor & Francis: 2011.

•  CitySim solver •  XML data exchange: GUI to solver

•  ASCII data exchange: solver to GUI

•  Solver coded in C++

CitySim solver structure

8

Haldi and Robinson, JBPS : 4(4), 2011

Results: A shoebox

A single building (Martigny)

A district (Neuchâtel)

No-MASS: framework

– Synthetic population generator – Appliance allocation / use

–  Large –  small

– Activities (homes) – Short absences (workplaces) – Long absenses – Location – Metabolic gains – Heating use (machine learning) – Hot water use – Use of shading – Use of window – Use of lights – Adaptive comfort – Social Interactions – BDI rules – Extension to DSM (and LVN)

Chapman, Siebers and Robinson, JBPS (under review), 2017

Typological sampling: InSmart Step 1) Define typologies and their distribution

Long, Alalwany & Robinson, Energy in Buildings, 2017 (in prep).

Step 2) Surveys and local sensitivity analysis

Insul UvalG SHGC Orient Inf Occ/App Stpnt Roof

Norm

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tivity

Inde

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0.0

1.0

2.0

Step 3) Synthetic stock creation

Step 4) Simulation and Monte Carlo assignment

Step 5) Visualisation and analysis

Total energy demand (Ward)

Total energy demand (Building)

InSmart: whole system simulation

InSmart: whole system simulation

UK generalisation: EnHub

An open-source approach

Sousa, Jones, Mirzaei and Robinson, Proc. Building Simulation 2017.

A01: Solid wall insulation A02: Loft insulation A03: Double glazing A04: Cylinder insulation A05: Draught proofing A06: Efficient electrical appliances

Dynamically simulating the UK Housing Stock using EnHub and a modest (x64) HPC takes approx. 7mins!

Conclusions

•  Microsimulation is data and processor hungry, but it is urban-sensitive and powerful.

•  Urban typological sampling is not (currently) urban sensitive, but is less hungry.

•  National typological sampling is adaptable to urban scales, but requires care.

•  Open solutions are needed: models AND data preparation workflows.

•  Where’s the real challenge and potential now?: integrated urban [phys+social] modelling!

Thank you

Acknowledgements

Many thanks to: Dr Gavin Long, Dr Ben Jones, Dr Parham Mirzaei, Dr Jacob Chapman, Ana-Sancho Tomás, Gustavo Sousa, Mazin Alalwany