End-to-End Heat-Pump Control Using Continuous Time Stochastic Modelling and Uppaal Stratego

Imran Riaz Hasrat; Peter Gjøl Jensen; Kim Guldstrand Larsen; Jiří Srba

doi:10.1007/978-3-031-10363-6_24

End-to-End Heat-Pump Control Using Continuous Time Stochastic Modelling and Uppaal Stratego

Imran Riaz Hasrat, Peter Gjøl Jensen^*, Kim Guldstrand Larsen, Jiří Srba

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

1 Citation (Scopus)

Abstract

Heatpump-based floor-heating systems for domestic heating offer flexibility in energy-consumption patterns, which can be utilized for reducing heating costs—in particular when considering hour-based electricity prices. Such flexibility is hard to exploit via classical Model Predictive Control (MPC), and in addition, MPC requires a priori calibration (i.e. model identification) which is often costly and becomes outdated as the dynamics and use of a building change. We solve these shortcomings by combining recent advancements in stochastic model identification and automatic (near-)optimal controller synthesis. Our method suggests an adaptive model-identification using the tool CTSM-R, and an efficient control synthesis based on Q-learning for Euclidean Markov Decision Processes via Uppaal Stratego. On a virtual Danish family-house from the OpSys project, we demonstrate up to 33% reduction in heating cost while retaining comparable comfort to a standard bang-bang controller. Furthermore, we show the flexibility of our method by computing the Pareto-frontier that visualizes the cost/comfort tradeoff.

Original language	English
Title of host publication	Theoretical Aspects of Software Engineering : 16th International Symposium, TASE 2022, Proceedings
Editors	Yamine Aït-Ameur, Florin Crăciun
Number of pages	18
Publisher	Springer
Publication date	2022
Pages	363-380
ISBN (Print)	9783031103629
DOIs	https://doi.org/10.1007/978-3-031-10363-6_24
Publication status	Published - 2022
Event	16th International Symposium on Theoretical Aspects of Software Engineering, TASE 2022 - Cluj-Napoca, Romania Duration: 8 Jul 2022 → 10 Jul 2022

Conference

Conference	16th International Symposium on Theoretical Aspects of Software Engineering, TASE 2022
Country/Territory	Romania
City	Cluj-Napoca
Period	08/07/2022 → 10/07/2022

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13299 LNCS
ISSN	0302-9743

Bibliographical note

Funding Information:
Acknowledgements. We would like to thank Simon Thorsteinsson for his extensive help with Dymola and acquiring base data for model identification. This research is partly funded by the ERC Advanced Grant LASSO, the Villum Investigator Grant S4OS as well as DIREC: Digital Research Centre Denmark.

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Floor heating
Heat-pump control
Model identification
Strategy syntheses

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10.1007/978-3-031-10363-6_24

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Cite this

Hasrat, Imran Riaz ; Jensen, Peter Gjøl ; Larsen, Kim Guldstrand et al. / End-to-End Heat-Pump Control Using Continuous Time Stochastic Modelling and Uppaal Stratego. Theoretical Aspects of Software Engineering: 16th International Symposium, TASE 2022, Proceedings. editor / Yamine Aït-Ameur ; Florin Crăciun. Springer, 2022. pp. 363-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13299 LNCS).

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title = "End-to-End Heat-Pump Control Using Continuous Time Stochastic Modelling and Uppaal Stratego",

abstract = "Heatpump-based floor-heating systems for domestic heating offer flexibility in energy-consumption patterns, which can be utilized for reducing heating costs—in particular when considering hour-based electricity prices. Such flexibility is hard to exploit via classical Model Predictive Control (MPC), and in addition, MPC requires a priori calibration (i.e. model identification) which is often costly and becomes outdated as the dynamics and use of a building change. We solve these shortcomings by combining recent advancements in stochastic model identification and automatic (near-)optimal controller synthesis. Our method suggests an adaptive model-identification using the tool CTSM-R, and an efficient control synthesis based on Q-learning for Euclidean Markov Decision Processes via Uppaal Stratego. On a virtual Danish family-house from the OpSys project, we demonstrate up to 33% reduction in heating cost while retaining comparable comfort to a standard bang-bang controller. Furthermore, we show the flexibility of our method by computing the Pareto-frontier that visualizes the cost/comfort tradeoff.",

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note = "Funding Information: Acknowledgements. We would like to thank Simon Thorsteinsson for his extensive help with Dymola and acquiring base data for model identification. This research is partly funded by the ERC Advanced Grant LASSO, the Villum Investigator Grant S4OS as well as DIREC: Digital Research Centre Denmark. Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 16th International Symposium on Theoretical Aspects of Software Engineering, TASE 2022 ; Conference date: 08-07-2022 Through 10-07-2022",

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Hasrat, IR, Jensen, PG , Larsen, KG & Srba, J 2022, End-to-End Heat-Pump Control Using Continuous Time Stochastic Modelling and Uppaal Stratego. in Y Aït-Ameur & F Crăciun (eds), Theoretical Aspects of Software Engineering: 16th International Symposium, TASE 2022, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13299 LNCS, pp. 363-380, 16th International Symposium on Theoretical Aspects of Software Engineering, TASE 2022, Cluj-Napoca, Romania, 08/07/2022. https://doi.org/10.1007/978-3-031-10363-6_24

End-to-End Heat-Pump Control Using Continuous Time Stochastic Modelling and Uppaal Stratego. / Hasrat, Imran Riaz; Jensen, Peter Gjøl ; Larsen, Kim Guldstrand et al.
Theoretical Aspects of Software Engineering: 16th International Symposium, TASE 2022, Proceedings. ed. / Yamine Aït-Ameur; Florin Crăciun. Springer, 2022. p. 363-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13299 LNCS).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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AU - Srba, Jiří

N1 - Funding Information: Acknowledgements. We would like to thank Simon Thorsteinsson for his extensive help with Dymola and acquiring base data for model identification. This research is partly funded by the ERC Advanced Grant LASSO, the Villum Investigator Grant S4OS as well as DIREC: Digital Research Centre Denmark. Publisher Copyright: © 2022, Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Heatpump-based floor-heating systems for domestic heating offer flexibility in energy-consumption patterns, which can be utilized for reducing heating costs—in particular when considering hour-based electricity prices. Such flexibility is hard to exploit via classical Model Predictive Control (MPC), and in addition, MPC requires a priori calibration (i.e. model identification) which is often costly and becomes outdated as the dynamics and use of a building change. We solve these shortcomings by combining recent advancements in stochastic model identification and automatic (near-)optimal controller synthesis. Our method suggests an adaptive model-identification using the tool CTSM-R, and an efficient control synthesis based on Q-learning for Euclidean Markov Decision Processes via Uppaal Stratego. On a virtual Danish family-house from the OpSys project, we demonstrate up to 33% reduction in heating cost while retaining comparable comfort to a standard bang-bang controller. Furthermore, we show the flexibility of our method by computing the Pareto-frontier that visualizes the cost/comfort tradeoff.

AB - Heatpump-based floor-heating systems for domestic heating offer flexibility in energy-consumption patterns, which can be utilized for reducing heating costs—in particular when considering hour-based electricity prices. Such flexibility is hard to exploit via classical Model Predictive Control (MPC), and in addition, MPC requires a priori calibration (i.e. model identification) which is often costly and becomes outdated as the dynamics and use of a building change. We solve these shortcomings by combining recent advancements in stochastic model identification and automatic (near-)optimal controller synthesis. Our method suggests an adaptive model-identification using the tool CTSM-R, and an efficient control synthesis based on Q-learning for Euclidean Markov Decision Processes via Uppaal Stratego. On a virtual Danish family-house from the OpSys project, we demonstrate up to 33% reduction in heating cost while retaining comparable comfort to a standard bang-bang controller. Furthermore, we show the flexibility of our method by computing the Pareto-frontier that visualizes the cost/comfort tradeoff.

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Hasrat IR, Jensen PG , Larsen KG , Srba J. End-to-End Heat-Pump Control Using Continuous Time Stochastic Modelling and Uppaal Stratego. In Aït-Ameur Y, Crăciun F, editors, Theoretical Aspects of Software Engineering: 16th International Symposium, TASE 2022, Proceedings. Springer. 2022. p. 363-380. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13299 LNCS). doi: 10.1007/978-3-031-10363-6_24