Introducing the

Holistic Urban Energy Simulation platform

(HUES)

Dr Ralph Evins Urban Energy Systems Laboratory, Empa &

Chair of Building Physics, ETH, Zürich

‒ Features and components

‒ Multi-model ecology

‒ Semantic wiki

‒ Platform structure

‒ Example

‒ Implementation

‒ Development timeline

Overview

“(Co-)simulation platform to enable interaction between new and existing programs to predict the energy demand, generation, storage and management of groups of buildings.” ‒ use of GIS and BIM data ‒ modular structure ‒ open source ‒ extensible

Key features

‒ research-level usability

‒ no front end / graphical interface ‒ no out-of-the-box functionality

‒ modules are selected and combined as needed ‒ never all used at once

Data

Script

Integrated model

Controller

Use profiles

Building

Longwave radiation

Energy hub

Rolling horizons

Building configuration

Optimisation suite

BIM data

Embedded controller

Network

Cost database

Evaluation function

Network configuration

Urban configuration

GIS data

Energy hub controller

Multiple hubs

Hub configuration CFD

Plant, Storage

Solar gains

Hydraulics

Stochastic models

Building controller

Microclimate emulator

HUES platform structure overview

‒ building demand models ‒ building energy systems ‒ occupancy and occupant behaviour ‒ urban energy flows (heating / cooling networks, electricity micro-grids, hubs) ‒ renewable potentials (solar thermal, PV, …) ‒ ground coupling (boreholes, ground storage) ‒ urban microclimate, outdoor comfort and air quality ‒ controls ‒ embodied energy ‒ social and economic factors ‒ [other urban resources], [transport modelling]

Components

initiated planned / in progress

not yet determined

Multi-model ecology a group of models and datasets co-evolving within the context of a dynamic socio-technical environment

Aims to facilitate the cultivation of a set of resources that can be configured and reconfigured in different ways to address a problem from different perspectives, and which evolves over time as knowledge and needs change.

LA Bollinger, I Nikolic, CB Davis and GPJ Dijkema (forthcoming, 2015). Multi-model ecologies: cultivating model ecosystems in industrial ecology. Journal of Industrial Ecology.

‒ Use open standards and open software ‒ Document, and use documentation standards ‒ Build simple components (represent complexity modularly) ‒ Leverage the Web, but recognize its limitations ‒ Prioritize flexibility over completeness ‒ Borrow proudly, acknowledge your role

Current ontological structure

Aim To enable efficient navigation of information about models and datasets, facilitating model reuse and integration

Semantic Wiki = Wiki + Graph Database

collecting distributed and changing knowledge

describing structure: subject-predicate-object

Facilitating model reuse and integration in the urban energy simulation platform L. Andrew Bollinger and Ralph Evins Submitted to Simulation of Large-scale Complex Urban Systems conference, Reykjavik, Iceland, June 2015

Uses / benefits ‘Bringing the platform together’: ‒ Resources located in different places / formats Collecting scripts and datasets that were used together for a specific end use (e.g. a case study or a publication) Classing scripts and datasets to make them easier to find / reuse Automating (some) documentation: ‒ Deriving interactions directly from code ‒ Retrieving meta-data & comments from code

Set of Python & MatLab scripts that link existing simulation programs ‒ Modular, extensible, cluster-deployable ‒ Flexible: reconfigurable to serve many use-cases (no single platform…) ‒ Reusability through use of common structure (edit, run, retrieve….) ‒ Compatible with Git ( / BitBucket / SourceTree) for cloud-based version-tracking

Why Python & MatLab? ‒ Common tools: new people are likely to be familiar with one or the other ‒ (Relatively) easy to use, but (relatively) powerful ‒ Extends existing codebase ‒ Avoid other languages (where possible) to keep it simple

Implementation

‒ Fully open-source (free for industry) ‒ More powerful (in rare circumstances) ‒ New(ish), rising popularity

‒ Available for academics (costly for industry) ‒ Easier to use (depending on experience) ‒ More established

IDA ICE

Implementation

HUES scripts

Existing programs

Cluster deployment

Data

Script

Integrated model

Controller

Use profiles

Building

Longwave radiation

Energy hub

Rolling horizons

Building configuration

Optimisation suite

BIM data

Embedded controller

Network

Cost database

Evaluation function

Network configuration

Urban configuration

GIS data

Energy hub controller

Multiple hubs

Hub configuration CFD

Plant, Storage

Solar gains

Hydraulics

Stochastic models

Building controller

Microclimate emulator

HUES platform structure overview

Use profiles

Building

Longwave radiation

Energy hub

Rolling horizons

Building configuration

Optimisation suite

Network

Cost database

Evaluation function

Multiple hubs

Hub configuration

Data

Script

Integrated model

Controller

Stochastic models

HUES v1.0 (March 2015)

Use profiles

Building

Longwave radiation

Energy hub

Rolling horizons

Building configuration

Optimisation suite

Network

Cost database

Evaluation function

Multiple hubs

Hub configuration

Data

Script

Integrated model

Controller

Stochastic models

HUES v1.0 (March 2015)

Use profiles

Building

Energy hub

Building configuration

Optimisation suite

Cost database

Evaluation function

Hub configuration

Stochastic models

Example: impact of inter-building variability on energy hub sizing

Run

Edit

Read

Run

Edit

Read

Write

Read

Geometric

Non-geometric

Use profiles

Building

Longwave radiation

Energy hub

Rolling horizons

Building configuration

Optimisation suite

BIM data

Embedded controller

Network

Cost database

Evaluation function

Network configuration

Urban configuration

GIS data

Energy hub controller

Multiple hubs

Hub configuration CFD

Plant, Storage

Solar gains

Hydraulics

Stochastic models

Building controller

Microclimate emulator

HUES platform structure overview

The Rhinoceros ecosystem

3D modelling

Generative / parametric design

Environmental analysis Bespoke modules

/ plugins

Optimisation

BIM & GIS import

GG

v1.0 March 2015 ‒ Initial version consisting of basic content from Empa UES lab ‒ Available via BitBucket ‒ Accompanied by initial semantic wiki ‒ Guidance for users (particularly on associated software requirements) ‒ Guidance for contributors

First release

v1.1 – 1.9 releases ‒ Ongoing development of existing components ‒ Addition of new modules / areas ‒ Contributions from others

v2.0 release: January 2016 ‒ ‘Final’ version of SCCER platform

2017 – 2020 ‒ What will be needed for v3.0?

Future development

Urban Energy Systems lab Platform co-ordination: Dr Andrew Bollinger / Dr Ralph Evins Optimisation: Christoph Waibel Control: Marc Hohmann Energy hub: Dr Keno Omu / Georgios Mavromatidis Network: Julien Marquant Electrical: Boran Morvaj GIS: Somil Miglani Linux cluster support: Patrik Burkhalter Future contributors:

+ …?

Acknowledgements / contacts