A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Diyan, Muhammad; Silva, Bhagya Nathali; Han, Kijun

doi:10.3390/s20123450

Cited by 21 publications

(23 citation statements)

References 50 publications

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“…Apart from MARL, multi-objective learning also received research attention considering different control objectives in BEMS. A multi-objective algorithm is proposed based on human appliances interaction, which considers scheduling in the context of energy consumption and discomfort level of the home user [17].…”

Section: B Deep Rl Methods In Bemsmentioning

confidence: 99%

Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey

Zhang¹,

Seal²,

Wu³

et al. 2022

IEEE Access

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Building energy management has been recognized as one of the core problems in modern power grids with respect to system operation efficiency. However, the building energy management system (BEMS) is now facing more challenges and uncertainties with the increasing penetration of renewable energy and increasing adoption of different types of electrical appliances and equipment. Classical model predictive control (MPC) has shown effective in building energy management, although it suffers from labour-intensive modelling and complex online control optimization. Recently, with the growing accessibility to building control and automation data, data-driven solutions such as data-driven MPC and reinforcement learning (RL)-based methods have attracted more research interest. However, the potential of integrating these two types of methods and how to choose suitable control algorithms have not been well discussed. In this work, we first present a compact review of the recent advances in data-driven MPC and RL-based control methods for building energy management. Furthermore, the main challenges in these approaches and general discussions on the selection of control methods are discussed.INDEX TERMS Building energy management, model predictive control, reinforcement learning, datadriven control.

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Section: B Deep Rl Methods In Bemsmentioning

confidence: 99%

Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey

Zhang¹,

Seal²,

Wu³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: B Deep Rl Methods In Bemsmentioning

confidence: 99%

Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey

Zhang¹,

Seal²,

Wu³

et al. 2021

Preprint

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Building energy management is one of the core problems in modern power grids to reduce energy consumption while ensuring occupants' comfort. However, the building energy management system (BEMS) is now facing more challenges and uncertainties with the increasing penetration of renewables and complicated interactions between humans and buildings. Classical model predictive control (MPC) has shown its capacity to reduce building energy consumption, but it suffers from laborintensive modelling and complex on-line control optimization. Recently, with the growing accessibility to the building control and automation data, data-driven solutions have attracted more research interest. This paper presents a compact review of the recent advances in data-driven MPC and reinforcement learning based control methods for BEMS. The main challenges in these approaches and insights on the selection of a control method are discussed.

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“…A composition of deep learning models for neural networks and rule based systems to control home temperature was discussed in [227]. The reinforcement learning model for usage scheduling was developed in [228]. The study proposed energy management necessary to implement an efficient scheduling algorithm working in real-time mode.…”

Section: Optimal Energy Management and Sustainabilitymentioning

confidence: 99%

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

et al. 2021

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The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this paper, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry. INDEX TERMS Internet of Things (IoT), smart home, healthcare, energy management, security. I. INTRODUCTION O VER time, the type of home was evolving. The shape of the house was evolving during ages, cultural revolutions, and climate changes. We have divided our homes into rooms devoted to sleeping, cooking, work, children, sanitary needs, warehousing, etc. People started to settle in various locations and continents.

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A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Cited by 21 publications

References 50 publications

Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey

Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey

Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

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