Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand...

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Autores principales:	Kadir Amasyali, Jeffrey Munk, Kuldeep Kurte, Teja Kuruganti, Helia Zandi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep Q-networks reinforcement learning heat pump water heater demand response smart grid machine learning Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/eaa8972ca3004a5e8db38130c5d468d7
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spelling	oai:doaj.org-article:eaa8972ca3004a5e8db38130c5d468d72021-11-25T17:00:17ZDeep Reinforcement Learning for Autonomous Water Heater Control10.3390/buildings111105482075-5309https://doaj.org/article/eaa8972ca3004a5e8db38130c5d468d72021-11-01T00:00:00Zhttps://www.mdpi.com/2075-5309/11/11/548https://doaj.org/toc/2075-5309Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.Kadir AmasyaliJeffrey MunkKuldeep KurteTeja KurugantiHelia ZandiMDPI AGarticledeep Q-networksreinforcement learningheat pump water heaterdemand responsesmart gridmachine learningBuilding constructionTH1-9745ENBuildings, Vol 11, Iss 548, p 548 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep Q-networks reinforcement learning heat pump water heater demand response smart grid machine learning Building construction TH1-9745
spellingShingle	deep Q-networks reinforcement learning heat pump water heater demand response smart grid machine learning Building construction TH1-9745 Kadir Amasyali Jeffrey Munk Kuldeep Kurte Teja Kuruganti Helia Zandi Deep Reinforcement Learning for Autonomous Water Heater Control
description	Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.
format	article
author	Kadir Amasyali Jeffrey Munk Kuldeep Kurte Teja Kuruganti Helia Zandi
author_facet	Kadir Amasyali Jeffrey Munk Kuldeep Kurte Teja Kuruganti Helia Zandi
author_sort	Kadir Amasyali
title	Deep Reinforcement Learning for Autonomous Water Heater Control
title_short	Deep Reinforcement Learning for Autonomous Water Heater Control
title_full	Deep Reinforcement Learning for Autonomous Water Heater Control
title_fullStr	Deep Reinforcement Learning for Autonomous Water Heater Control
title_full_unstemmed	Deep Reinforcement Learning for Autonomous Water Heater Control
title_sort	deep reinforcement learning for autonomous water heater control
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/eaa8972ca3004a5e8db38130c5d468d7
work_keys_str_mv	AT kadiramasyali deepreinforcementlearningforautonomouswaterheatercontrol AT jeffreymunk deepreinforcementlearningforautonomouswaterheatercontrol AT kuldeepkurte deepreinforcementlearningforautonomouswaterheatercontrol AT tejakuruganti deepreinforcementlearningforautonomouswaterheatercontrol AT heliazandi deepreinforcementlearningforautonomouswaterheatercontrol
_version_	1718412810932715520

Deep Reinforcement Learning for Autonomous Water Heater Control

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