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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