A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper...

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Autores principales:	Christian Blad, Simon Bøgh, Carsten Kallesøe
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep reinforcement learning artificial intelligence HVAC-systems underfloor heating energy in buildings predictive analytics Technology T
Acceso en línea:	https://doaj.org/article/8030b101c8894434b618d3475c0b545a
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spelling	oai:doaj.org-article:8030b101c8894434b618d3475c0b545a2021-11-25T17:25:56ZA Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems10.3390/en142274911996-1073https://doaj.org/article/8030b101c8894434b618d3475c0b545a2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7491https://doaj.org/toc/1996-1073This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method.Christian BladSimon BøghCarsten KallesøeMDPI AGarticledeep reinforcement learningartificial intelligenceHVAC-systemsunderfloor heatingenergy in buildingspredictive analyticsTechnologyTENEnergies, Vol 14, Iss 7491, p 7491 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep reinforcement learning artificial intelligence HVAC-systems underfloor heating energy in buildings predictive analytics Technology T
spellingShingle	deep reinforcement learning artificial intelligence HVAC-systems underfloor heating energy in buildings predictive analytics Technology T Christian Blad Simon Bøgh Carsten Kallesøe A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems
description	This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method.
format	article
author	Christian Blad Simon Bøgh Carsten Kallesøe
author_facet	Christian Blad Simon Bøgh Carsten Kallesøe
author_sort	Christian Blad
title	A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems
title_short	A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems
title_full	A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems
title_fullStr	A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems
title_full_unstemmed	A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems
title_sort	multi-agent reinforcement learning approach to price and comfort optimization in hvac-systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8030b101c8894434b618d3475c0b545a
work_keys_str_mv	AT christianblad amultiagentreinforcementlearningapproachtopriceandcomfortoptimizationinhvacsystems AT simonbøgh amultiagentreinforcementlearningapproachtopriceandcomfortoptimizationinhvacsystems AT carstenkallesøe amultiagentreinforcementlearningapproachtopriceandcomfortoptimizationinhvacsystems AT christianblad multiagentreinforcementlearningapproachtopriceandcomfortoptimizationinhvacsystems AT simonbøgh multiagentreinforcementlearningapproachtopriceandcomfortoptimizationinhvacsystems AT carstenkallesøe multiagentreinforcementlearningapproachtopriceandcomfortoptimizationinhvacsystems
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A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems

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