Coupling physical and machine learning models: case study of a single-family house

Basak Falay; Sandra Wilfling; Qamar Alfalouji; Johannes Exenberger; Thomas Schranz; Christian Møldrup Legaard; Ingo Leusbrock; Gerald Schweiger

doi:10.3384/ecp21181335

Coupling physical and machine learning models: case study of a single-family house

Basak Falay, Sandra Wilfling, Qamar Alfalouji, Johannes Exenberger, Thomas Schranz, Christian Møldrup Legaard, Ingo Leusbrock, Gerald Schweiger

Institute of Software Technology (7160)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Future intelligent and integrated energy systems must have a high degree of flexibility and efficiency to ensure reliable and sustainable operation. Along with the rapid expansion of renewable energy, this degree of flexibility and efficiency can be achieved by overcoming the clear separation between different sectors and by increasing connectivity and the associated data availability through the integration of sensors and edge/fog computing. All of these developments drive the transition from towards so-called Cyber-Physical Energy Systems . The Cyber technologies (sensors, edge/fog computing, IoT networks, etc.) are able to monitor the physical systems, to enable communication between different subsystems and to control them. The emergence of Cyber-Physical Systems poses new challenges for traditional modelling and simulation approaches.

Original language	English
Title of host publication	Proceedings of 14th Modelica Conference 2021
Editors	Martin Sjölund, Lena Buffoni, Adrian Pop, Lennart Ochel
Place of Publication	Linköping
Pages	335-341
Number of pages	7
DOIs	https://doi.org/10.3384/ecp21181335
Publication status	Published - 27 Sept 2021
Event	14th International Modelica Conference - Virtuell, Sweden Duration: 20 Sept 2021 → 24 Dec 2021 https://2021.international.conference.modelica.org/

Conference

Conference	14th International Modelica Conference
Country/Territory	Sweden
City	Virtuell
Period	20/09/21 → 24/12/21
Internet address	https://2021.international.conference.modelica.org/

Keywords

co-simulation
building
smart energy system

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3384/ecp21181335Licence: CC BY 4.0

Cite this

Coupling physical and machine learning models: case study of a single-family house. / Falay, Basak; Wilfling, Sandra; Alfalouji, Qamar et al.
Proceedings of 14th Modelica Conference 2021. ed. / Martin Sjölund; Lena Buffoni; Adrian Pop; Lennart Ochel. Linköping, 2021. p. 335-341.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Falay, B, Wilfling, S, Alfalouji, Q , Exenberger, J, Schranz, T, Møldrup Legaard, C, Leusbrock, I & Schweiger, G 2021, Coupling physical and machine learning models: case study of a single-family house. in M Sjölund, L Buffoni, A Pop & L Ochel (eds), Proceedings of 14th Modelica Conference 2021. Linköping, pp. 335-341, 14th International Modelica Conference, Virtuell, Sweden, 20/09/21. https://doi.org/10.3384/ecp21181335

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abstract = "Future intelligent and integrated energy systems must have a high degree of flexibility and efficiency to ensure reliable and sustainable operation. Along with the rapid expansion of renewable energy, this degree of flexibility and efficiency can be achieved by overcoming the clear separation between different sectors and by increasing connectivity and the associated data availability through the integration of sensors and edge/fog computing. All of these developments drive the transition from towards so-called Cyber-Physical Energy Systems . The Cyber technologies (sensors, edge/fog computing, IoT networks, etc.) are able to monitor the physical systems, to enable communication between different subsystems and to control them. The emergence of Cyber-Physical Systems poses new challenges for traditional modelling and simulation approaches.",

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AB - Future intelligent and integrated energy systems must have a high degree of flexibility and efficiency to ensure reliable and sustainable operation. Along with the rapid expansion of renewable energy, this degree of flexibility and efficiency can be achieved by overcoming the clear separation between different sectors and by increasing connectivity and the associated data availability through the integration of sensors and edge/fog computing. All of these developments drive the transition from towards so-called Cyber-Physical Energy Systems . The Cyber technologies (sensors, edge/fog computing, IoT networks, etc.) are able to monitor the physical systems, to enable communication between different subsystems and to control them. The emergence of Cyber-Physical Systems poses new challenges for traditional modelling and simulation approaches.

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ER -

Coupling physical and machine learning models: case study of a single-family house

Abstract

Conference

Keywords

UN SDGs

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Cite this