Real-Time Residential Demand Response

Li, Hepeng; Wan, Zhiqiang; He, Haibo

doi:10.1109/tsg.2020.2978061

Cited by 146 publications

(56 citation statements)

References 26 publications

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“…The DDPGs-based system takes action concerning the charging/discharging of Energy Storage System (ESS) and HVAC power consumption, considering the current observation information. Li et al [39] presented a deep reinforcement learning (DRL) technique to schedule the household appliances, taking into account the user behavior, energy price, and outdoor temperature. The DRL used in this scheme takes care of discrete and continuous power level actions which enable the scheduling of distinct load of appliances.…”

Section: Related Workmentioning

confidence: 99%

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

Diyan

Silva

Han

2020

Sensors

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Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

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Section: Related Workmentioning

confidence: 99%

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

Diyan

Silva

Han

2020

Sensors

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“…However, both discrete and continuous actions appear in practical residential energy management. To support discrete and continuous actions simultaneously, Li et al proposed a TRPO-based approach to jointly optimize the schedules of different types of appliances in a smart home, e.g., HVAC systems, EWHs, EVs, DWs, WMs, clothes dryers (CDs), a refrigerator, and a hairdryer [59]. When the number of smart homes is increasing, the scheduling of all energy subsystems would be more difficult since more coupling constraints and control decisions should be considered.…”

Section: A Multiple Energy Subsystems In Residential Buildingsmentioning

confidence: 99%

“…Therefore, it is worthwhile to design DRL-based energy management algorithms for building microgrids with the consideration of load flexibility. Since there are both discrete and continuous variables in energy optimization problem of building microgrids with HVAC loads, the designed DRL-based algorithms should support different kinds of actions as in [59]. In addition, the designed DRL-based algorithm should be scalable since the number of HVAC systems in residential building microgrids or the number of zones served by an HVAC system in commercial building microgrids is large.…”

Section: E Drl-based Energy Optimization For Building Microgridsmentioning

confidence: 99%

Deep Reinforcement Learning for Smart Home Energy Management

Xie

et al. 2020

IEEE Internet Things J.

308

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In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

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“…RL-based approaches are proposed for minimizing the cost of smart appliances [68] and shiftable loads [69]. In order to learn the optimal demand response scheduling strategy of household appliances, [70] proposes a model-free DRL method, which does not require the concrete distribution of the appliance data, electricity price, and outdoor temperature. The DNN is trained by TRPO and can effectively learn from real-time data of residential appliances.…”

Section: B Demand-side Managementmentioning

confidence: 99%

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Cao

Zhao

et al. 2020

Journal of Modern Power Systems and Clean Energy

263

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With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

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Real-Time Residential Demand Response

Cited by 146 publications

References 26 publications

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

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

Deep Reinforcement Learning for Smart Home Energy Management

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

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