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Behavior Tree based Control Strategies for Resilient Heat Pump Operation in Residential Buildings

Urban, Piet und Klement, Peter und Schlüters, Sunke und Schönfeldt, Patrik (2025) Behavior Tree based Control Strategies for Resilient Heat Pump Operation in Residential Buildings. Energy Reports, Vol.13, Seiten 1054-1068. Elsevier. doi: 10.1016/j.egyr.2024.12.039. ISSN 2352-4847.

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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S2352484724008552?via%3Dihubhttps://ssrn.com/abstract=4951682

Kurzfassung

Behavior trees are a proven concept in the creation of complex task-switching control and artificial intelligence for robotic systems and non-player characters in the computer games industry. Requirements such as flexibility, maintainability, reusability of functionalities or expandability also apply to the control of decentralised energy systems. Despite this, there is a noticeable research gap regarding the application of behavior trees in that sector. Based on a foundational heating system, including thermodynamic modelling of a part-load capable heat pump with TESPy, tree structures for its control are created using the Python library py_trees for implementation. With a view to minimising the annual operational performance indicators electricity price and CO2 emissions, which reflect the optimal use of renewable shares, several control strategies are compared. We identify and illustrate the principal limitations of decision trees, mixed-integer linear optimisation performed with oemof-solph, as well as a classic rule-based approach. The proposed higher-level behavior tree combines the strengths of such approaches whilst pursuing the additional target of reducing the start-up and associated wear of the heat pump without significantly increasing the computation time.

elib-URL des Eintrags:https://elib.dlr.de/206397/
Dokumentart:Zeitschriftenbeitrag
Titel:Behavior Tree based Control Strategies for Resilient Heat Pump Operation in Residential Buildings
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Urban, Pietpiet.urban (at) dlr.dehttps://orcid.org/0009-0004-5259-5891175093613
Klement, PeterPeter.Klement (at) dlr.dehttps://orcid.org/0000-0001-7175-6145NICHT SPEZIFIZIERT
Schlüters, Sunkesunke.schlueters (at) dlr.dehttps://orcid.org/0000-0002-2186-812XNICHT SPEZIFIZIERT
Schönfeldt, PatrikPatrik.Schoenfeldt (at) dlr.dehttps://orcid.org/0000-0002-4311-2753NICHT SPEZIFIZIERT
Datum:2 Januar 2025
Erschienen in:Energy Reports
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:Vol.13
DOI:10.1016/j.egyr.2024.12.039
Seitenbereich:Seiten 1054-1068
Verlag:Elsevier
Name der Reihe:Elsevier Energy Reports
ISSN:2352-4847
Status:veröffentlicht
Stichwörter:Behavior Tree, Decision Tree, CART, Optimisation, MILP, Heat Pump, Energy Management, Machine Learning
HGF - Forschungsbereich:Energie
HGF - Programm:Energiesystemdesign
HGF - Programmthema:Digitalisierung und Systemtechnologie
DLR - Schwerpunkt:Energie
DLR - Forschungsgebiet:E SY - Energiesystemtechnologie und -analyse
DLR - Teilgebiet (Projekt, Vorhaben):E - Energiesystemtechnologie
Standort: Oldenburg
Institute & Einrichtungen:Institut für Vernetzte Energiesysteme > Energiesystemtechnologie
Hinterlegt von: Urban, Piet
Hinterlegt am:25 Sep 2024 10:58
Letzte Änderung:09 Jan 2025 14:53

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