Towards an integrated design of heat pump systems: Application of process intensification using two-stage optimization

Vering, Christian; Wüllhorst, Fabian; Mehrfeld, Philipp; Müller, Dirk

doi:10.1016/j.enconman.2021.114888

Cited by 21 publications

(32 citation statements)

References 41 publications

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“…For specified parameters, records and structural parameter options, we refer to the source code. We highlight that further examples for different use cases based on previous work exist in the library as well (Vering et al 2021;Wüllhorst et al 2022).…”

Section: System Layoutmentioning

confidence: 87%

“…For design, all domain-dependencies of renewable building energy systems are regarded in BESMod. Thus, simulation based design optimization methods as in (Vering et al 2021) should be extended towards sustainable design strategies for building energy systems.…”

Section: Discussionmentioning

confidence: 99%

“…Direct electric heating, bivalent heat pumps, gas boilers, solar thermal systems, different storage options, and radiator as well as underfloor heating subsystems may be combined. As control system, several approaches based on Vering et al (2021) and the corresponding state of the art for the control of bivalent heat pump systems are implemented, too.…”

Section: Hydraulic Subsystemmentioning

confidence: 99%

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BESMod - A Modelica Library providing Building Energy System Modules

Wüllhorst¹,

Maier²,

Jansen³

et al. 2023

Linköping Electronic Conference Proceedings

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Towards the analysis and optimization of a coupled building energy sector, various component model libraries for hydraulic, ventilation, electrical, control, and building domains exist. However, no uniform open-source framework to couple these domains in a holistic building energy system simulation exists. Thus, we present BESMod, an open-source Modelica library, providing a modular approach towards domain-coupled building energy system simulations. BESMod relies on existing component specialized model libraries for the underlying physics. For the analysis of complex system simulations, user-friendly parameterization, consistent model interfaces, precalculated KPIs and debugging options are applied. The library is available at www.github.com/RWTH-EBC/BESMod. This paper motivates the library, lays out the interaction with existing model libraries and the general modular approach. An exemplary use case demonstrates the applicability of BESMod. Concluding, we motivate future development options.

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Section: System Layoutmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Hydraulic Subsystemmentioning

confidence: 99%

See 1 more Smart Citation

BESMod - A Modelica Library providing Building Energy System Modules

Wüllhorst¹,

Maier²,

Jansen³

et al. 2023

Linköping Electronic Conference Proceedings

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“…The problems are also considered 'black-box', meaning that no exact mathematical formulation is available that could be exploited. Examples are designing heat pump systems [16] and diabetes drug manufacturing [17]. The expensive part of the optimisation problem is approximated with a surrogate model in order to reduce the number of expensive computations.…”

Section: Surrogate-based Optimisationmentioning

confidence: 99%

“…The question of robustness and generalisation was also often encountered. Just because a surrogate model is accurate in one situation, does not mean it is accurate in similar situations, such as different weather conditions when designing heat pump systems [16]. In the ML community this question has gained a lot of attention in the past, but in SBO, generalisation aspects are less well understood.…”

Section: Open Questionsmentioning

confidence: 99%

A Survey on Sustainable Surrogate-Based Optimisation

Bliek

2022

Sustainability

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Surrogate-based optimisation (SBO) algorithms are a powerful technique that combine machine learning and optimisation to solve expensive optimisation problems. This type of problem appears when dealing with computationally expensive simulators or algorithms. By approximating the expensive part of the optimisation problem with a surrogate, the number of expensive function evaluations can be reduced. This paper defines sustainable SBO, which consists of three aspects: applying SBO to a sustainable application, reducing the number of expensive function evaluations, and considering the computational effort of the machine learning and optimisation parts of SBO. The paper reviews sustainable applications that have successfully applied SBO over the past years, and analyses the used framework, type of surrogate used, sustainable SBO aspects, and open questions. This leads to recommendations for researchers working on sustainability-related applications who want to apply SBO, as well as recommendations for SBO researchers. It is argued that transparency of the computation resources used in the SBO framework, as well as developing SBO techniques that can deal with a large number of variables and objectives, can lead to more sustainable SBO.

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