Co-Simulation between detailed building energy performance simulation and Modelica HVAC component models

Nicolai, Andreas; Paepcke, Anne

doi:10.3384/ecp1713263

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…A work close to ours is the NANDRAND simulation platform [55] which provides and simulates ready-to-use models to measure energy performance of physical buildings. Such models can be exported as FMUs compliant to FMI 2.0 and support get/set state functionality.…”

Section: Related Workmentioning

confidence: 99%

Reconciling interoperability with efficient Verification and Validation within open source simulation environments

Sinisi

Alimguzhin

Mancini

et al. 2021

Simulation Modelling Practice and Theory

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A Cyber-Physical System (CPS) comprises physical as well as software subsystems. Simulation-based approaches are typically used to support design and Verification and Validation (V&V) of CPSs in several domains such as: aerospace, defence, automotive, smart grid and healthcare. Accordingly, many simulation-based tools are available to support CPS design. This, on one side, enables designers to choose the toolchain that best suits their needs, on the other side poses huge interoperability challenges when one needs to simulate CPSs whose subsystems have been designed and modelled using different toolchains. To overcome such an interoperability problem, in 2010 the Functional Mock-up Interface (FMI) has been proposed as an open standard to support both Model Exchange (ME) and Co-Simulation (CS) of simulation models created with different toolchains. FMI has been adopted by several modelling and simulation environments. Models adhering to such a standard are called Functional Mock-up Units (FMUs). Indeed FMUs play an essential role in defining complex CPSs through, e.g., the System Structure and Parametrization (SSP) standard.Simulation-based V&V of CPSs typically requires exploring different simulation scenarios (i.e., exogenous input sequences to the CPS under design). Many such scenarios have a shared prefix. Accordingly, to avoid simulating many times such shared prefixes, the simulator state at the end of a shared prefix is saved and then restored and used as a start state for the simulation of the next scenario. In this context, an important FMI feature is the capability to save and restore the internal FMU state on demand. This is crucial to increase efficiency of simulation-based V&V. Unfortunately, the implementation of this feature is not mandatory and it is available only within some commercial software. As a result, the interoperability enabled by the FMI standard cannot be fully exploited for V&V when using open-source simulation environments.

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Section: Related Workmentioning

confidence: 99%

Reconciling interoperability with efficient Verification and Validation within open source simulation environments

Sinisi

Alimguzhin

Mancini

et al. 2021

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…Other tools, such as EnergyPlus, DOE-2, ESP-r, TRNSYS have all been compared in the literature (Sousa 2012;Wetter, Treeck, and Hensen 2013). These models are often coupled and run concurrently to make use of results generated by other models at runtime (Nicolai and Paepcke 2017). For example, a building energy simulation model computing room air temperatures may require heating loads from an HVAC supply system, with the latter coming from a simulation model external to the building simulation tool.…”

Section: Accelerating Building Simulation With Composable Surrogatesmentioning

confidence: 99%

Composing Modeling and Simulation with Machine Learning in Julia

Rackauckas¹,

Anantharaman²,

Edelman³

et al. 2021

Preprint

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In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build accelerated surrogates from component-based models, such as those conforming to the FMI standard, using continuous-time echo state networks (CTESN). The foundation of this environment, ModelingToolkit.jl, is an acausal modeling language which can compose the trained surrogates as components within its staged compilation process. As a complementary factor we present the JuliaSim model library, a standard library with differential-algebraic equations and pre-trained surrogates, which can be composed using the modeling system for design, optimization, and control. We demonstrate the effectiveness of the surrogate-accelerated modeling and simulation approach on HVAC dynamics by showing that the CTESN surrogates accurately capture the dynamics of a HVAC cycle at less than 4% error while accelerating its simulation by 340x. We illustrate the use of surrogate acceleration in the design process via global optimization of simulation parameters using the embedded surrogate, yielding a speedup of two orders of magnitude to find the optimum. We showcase the surrogate deployed in a co-simulation loop, as a drop-in replacement for one of the coupled FMUs, allowing engineers to effectively explore the design space of a coupled system. Together this demonstrates a workflow for automating the integration of machine learning techniques into traditional modeling and simulation processes.

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“…Future work could implement heat zoning (Dumont et al, 2015), with a limited number of more easily identifiable parameters to setup and calibrate the model, full transparency of the model could be achieved with an engineering equation solver (Bertagnolio, 2008). To reduce complexity and solving time in larger buildings with heat zoning, co -simulation with a validated building simulation program such as EnergyPlus has potential to improve the simulation efficiency and accuracy (Nicolai & Paepcke, 2017).…”

Section: Future Work and Conclusionmentioning

confidence: 99%

Opportunities for Communal Photovoltaic-Thermal Heating Systems with Storage

Kang¹,

Korolija²,

Rovas³

2019

Proceedings of the 2019 European Conference on Computing in Construction

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With 70% of the world's population projected to live in urban areas by 2060 (ARUP, 2016) and 67% of current energy related emissions produced in urban areas (IEA, 2008), there is a compelling case to investigate the reduction of CO2 emissions from heating in densely populated areas in the UK or other regions with heating requirements. This paper investigates how a s i n g l e PVT panel system with thermal and electrical storage could reduce heating emissions for a row of terraced houses. The main findings of the study carried out in Dymola/Modelica were that there is potential for greater thermal and electrical output with larger PVT systems and shared communal electrical and thermal storage. Preheating the mains water which supplies the hot water tank u s i n g the PVT both increased the PVT thermal efficiency and utilisation, and the electrical efficiency. In this configuration, the PVT system was able to supply all hot water demands of a row of terraced houses and supply about 91% of electricity demands.

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Co-Simulation between detailed building energy performance simulation and Modelica HVAC component models

Cited by 15 publications

References 4 publications

Reconciling interoperability with efficient Verification and Validation within open source simulation environments

Reconciling interoperability with efficient Verification and Validation within open source simulation environments

Composing Modeling and Simulation with Machine Learning in Julia

Opportunities for Communal Photovoltaic-Thermal Heating Systems with Storage

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