Template based code generation of  Modelica building energy simulation models

Nytsch‐Geusen, Christoph; Inderfurth, Alexander; Kaul, Werne; Mucha, Katharina; Rädler, Jörg; Thorade, Matthis; Tugores, Carles Ribas

doi:10.3384/ecp17132199

Cited by 8 publications

(8 citation statements)

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“…Future developments of the BIM2Modelica toolchain will focus on automatic reduction of the building model complexity dependent on the given boundary conditions (orientation of façade elements, conditions of use for the zones), similar as described in Reynders et al (2017). Further, additional specialized CoTeTo templates for C# code generation that supports a building model visualization for Unity (https://unity3d.com/de) are under development (see also Nytsch-Geusen et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The CoTeTo code generator for thermal multi-zone models code was designed for the predefined model classes (thermal zones, walls, windows, doors etc.) of the Modelica BuildingSystems library (Nytsch-Geusen et al, 2016). As other Modelica libraries for building energy simulation such as IDEAS, AIXLib and Buildings, the BuildingSystems library uses as its core the same Modelica IBPSA library (Modelica IBPSA library, 2019), which is the successor of the former Annex 60 library (Wetter et a.…”

Section: Buildingsystems Librarymentioning

confidence: 99%

“…On the one hand, the manual configuration of a thermal multi-zone building in Modelica in a graphical editor, based on components of a predefined library is an error-prone process. For example, the definition of a thirteen-zone building model in Modelica leads to a mo-file with more than 1,500 lines of code and a huge number of connect statements (Nytsch-Geusen, 2017). On the other hand, architects are using modelling tools such as ArchiCAD or Revit for their 3D building designs and often also Rhinoceros for prototypical designs.…”

Section: Introductionmentioning

confidence: 99%

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BIM2Modelica - An open source toolchain for generating and simulating thermal multi-zone building models by using structured data from BIM models

Nytsch‐Geusen

Rädler

Thorade³

et al. 2019

Linköping Electronic Conference Proceedings

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This contribution describes an open source toolchain which can transfer BIM models of 3D building constructions from CAAD programs into executable thermal multi-zone buildings models based on Modelica building energy simulation libraries. For this purpose, different open source libraries and tools were integrated into a Python-based software architecture of the toolchain: the IfcOpenShell/OCC libraries as the foundation for the import, analysis, and preparation of the BIM models; CoTeTo as the tool for the templatebased code generation of the Modelica building models; the BuildingSystems library as the base for the thermal multi-zone building models; and JModelica as the simulation tool to perform the simulation analyses. While the first part of the paper describes the general approach and the software architecture of the toolchain, the second part illustrates its application with an example of a real building.

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Section: Discussionmentioning

confidence: 99%

Section: Buildingsystems Librarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BIM2Modelica - An open source toolchain for generating and simulating thermal multi-zone building models by using structured data from BIM models

Nytsch‐Geusen

Rädler

Thorade³

et al. 2019

Linköping Electronic Conference Proceedings

Self Cite

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“…Generating Code from structured template files by replacing placeholders with often changing code is a helpful technique, applied in several Modelica projects (e.g. in (Nytsch Geusen et al, 2017)).…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the 2nd Japanese Modelica Conference Tokyo, Japan, May 17-18, 2018

2019

Linköping Electronic Conference Proceedings

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Model based product design requires both intuitive and effective user interface and powerful computing power. The presentation will contain a brief history of Modelica evolution and current status including some applications. Some new needs that are currently not covered will be discussed. New technical possibilities will be introduced, such as web apps for intuitive and effective user interaction and easy access, domain specific language extensions for advanced modeling capabilities and cloud computing for large scale simulation deployment..

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“…In the final step, the tool OnToMo extracts the information of the BrickModel and generates the corresponding Modelica model. We developed this tool using a code templating tool (CoTeTo) [25] and the SPARQL Protocol And RDF Query Language (SPARQL) [26].…”

Section: Transformation To Modelica Modelmentioning

confidence: 99%

Generating Generic Data Sets for Machine Learning Applications in Building Services Using Standardized Time Series Data

Stinner¹,

Yang²,

Schreiber³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Machine Learning Algorithms (ML) offer a high potential with low manual effort to discover appropriate energy efficiency measures for buildings. Although many building automation systems (BAS) record a high amount of data, technical systems such as boilers provide only a few data points per building. However, machine-learning algorithms require training based on a sufficient number of instances of a technical system in order to enable cross-building use. In contrast to electrical systems, few data sets of actual operation of thermal systems are publicly available. Since 2012, the monitoring system in our test object has continuously provided threshold-based data with a maximum resolution of 1 minute. We monitor the plants, energy consumption and comfort parameters with 9239 data points in total. In this paper, we show how our published data set from this building is structured. In order to facilitate the use of ML, each data point receives a uniform label according to a previously developed approach. Since the documentation of ML data sets varies in the building sector, we show an approach to standardize data sets with special datasheets for thermal systems to provide sufficient information for application of ML. We use the Brick Schema, a unified ontology standard for the description of topology in buildings, which is part of the future ASHRAE Standard 223P. We couple this with an approach we developed for the structured labeling of data points in buildings. We show how to semi-automatically generate physical models based on an open-source Modelica library from this ontology-based model. We show that the models, enriched with real time series data and data sheets, are in good agreement with the measured data. Finally, we show with an ML example that our approach based on Brick Schema and Modelica is able to deliver ML compliant data sets.

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Template based code generation of Modelica building energy simulation models

Cited by 8 publications

References 1 publication

BIM2Modelica - An open source toolchain for generating and simulating thermal multi-zone building models by using structured data from BIM models

BIM2Modelica - An open source toolchain for generating and simulating thermal multi-zone building models by using structured data from BIM models

Proceedings of the 2nd Japanese Modelica Conference Tokyo, Japan, May 17-18, 2018

Generating Generic Data Sets for Machine Learning Applications in Building Services Using Standardized Time Series Data

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