Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

Lee, Sangyoon; Choi, Dae-Hyun

doi:10.3390/s20072157

Cited by 95 publications

(49 citation statements)

References 32 publications

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“…It is shown that the energy bill can be reduced by 14%. Besides, a hierarchical deep reinforcement learning approach (based on the actor-critic algorithm) is proposed for scheduling the energy consumption of smart home applications and distributed energy resources (Lee and Choi, 2020).…”

Section: Application Of Machine Learning For the Battery Energy Storage Systemmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

116

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Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

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Section: Application Of Machine Learning For the Battery Energy Storage Systemmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

116

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“…Recent research presented a bi-level deep reinforcement learning approach for appliance scheduling. Besides, it incorporated charge and discharge schedules of energy storage and EV [127]. In [128], a load scheduling problem was solved via the Dijkstra algorithm, and the simulation results are compared with GA, Optimal Pattern Recognition Algorithm, and BPSO.…”

Section: Ec Based Dsmmentioning

confidence: 99%

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

et al. 2021

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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

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“…With the economic boom and technical advancement, an increasing number of smart devices find their way into people’s daily life, which have also received a lot of research attention from various communities like researchers, business and government. Lee et al [ 24 ] designed a two-level Deep Reinforcement Learning (DRL) framework for optimal energy management of smart homes. In the proposed simulation, two agents (an air conditioner and a washing machine) interact with each other to schedule the optimal home energy consumption efficiently.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach

Liu

Sun

2020

Sensors

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Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link congestion and data security. Thus, in the future, smart homes based on edge computing without using cloud center become an important research area. In this paper, we assume that all applications in a smart home environment are composed of edge nodes and users. In order to maximize the utility of users, we assume that all users and edge nodes are placed in a market and formulate a pricing resource allocation model with utility maximization. We apply the Lagrangian method to analyze the model, so an edge node (provider in the market) allocates its resources to a user (customer in the market) based on the prices of resources and the utility related to the preference of users. To obtain the optimal resource allocation, we propose a pricing-based resource allocation algorithm by using low-pass filtering scheme and conform that the proposed algorithm can achieve an optimum within reasonable convergence times through some numerical examples.

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Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

Cited by 95 publications

References 32 publications

Machine learning toward advanced energy storage devices and systems

Machine learning toward advanced energy storage devices and systems

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach

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