Real-time energy scheduling for home energy management systems with an energy storage system and electric vehicle based on a supervised-learning-based strategy

Huy, Truong Hoang Bao; Dinh, Huy Truong; Vo, Dieu Ngoc; Kim, Dae–Hee

doi:10.1016/j.enconman.2023.117340

Cited by 27 publications

(2 citation statements)

References 40 publications

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“…EDITED BY (Yin and Qin, 2022), home energy system management (HEMS) has become the most important aspect of achieving demand-side energy management in smart grids (Hafeez et al, 2021;Huy et al, 2023). The HEMS can make decisions for demand response based on current electricity prices, predicted photovoltaic output, user preferences, and device characteristics, achieving intelligent scheduling of home equipment and reducing electricity costs (Kikusato et al, 2019;Gomes et al, 2023).…”

Section: Open Accessmentioning

confidence: 99%

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Pan,

Zhu,

Luo

et al. 2024

Front. Therm. Eng.

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With the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. In this context, this paper proposes an optimization dispatching strategy for HEMS to reduce total cost with full consideration of uncertainties, while ensuring the users’ comfort. Firstly, a HEMS dispatching model is constructed to reasonably schedule the start/stop time of the dispatchable appliances and energy storage system to minimize the total cost for home users. Besides, this dispatching strategy also controls the switching time of temperature-controlled load such as air conditioning to reduce the energy consumption while maintaining the indoor temperature in a comfortable level. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called deep deterministic policy gradient. The example results verify the effectiveness and superiority of the proposed method. The energy cost can be effectively reduced by 21.9% at least compared with other benchmarks and the indoor temperature can be well maintained.

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Section: Open Accessmentioning

confidence: 99%

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Pan,

Zhu,

Luo

et al. 2024

Front. Therm. Eng.

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“…However, the current methods for optimizing HEMSs have drawbacks, like not accurately representing how appliances are used or what consumers want, leading to unreliable energy plans and needing a lot of computing power. To overcome these issues, this study suggests using a reinforcement learning (RL) approach that does not rely on existing data [12,13]. This research employs a novel method using deep learning (DL) to analyze energy management [14] systems in residential and commercial buildings.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning layout‐based optimal energy management in smart home: AI‐based approach

Afroosheh,

Esapour,

Khorram‐Nia

et al. 2024

IET Generation Trans & Dist

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This research addresses the pressing need for enhanced energy management in smart homes, motivated by the inefficiencies of current methods in balancing power usage optimization with user comfort. By integrating reinforcement learning and a unique column‐and‐constraint generation strategy, the study aims to fill this gap and offer a comprehensive solution. Furthermore, the increasing adoption of renewable energy sources like solar panels underscores the importance of developing advanced energy management techniques, driving the exploration of innovative approaches such as the one proposed herein. The constraint coordination game (CCG) method is designed to efficiently manage the power usage of each appliance, including the charging and discharging of the energy storage system. Additionally, a deep learning model, specifically a deep neural network, is employed to forecast indoor temperatures, which significantly influence the energy demands of the air conditioning system. The synergistic combination of the CCG method with deep learning‐based indoor temperature forecasting promises significant reductions in homeowner energy expenses while maintaining optimal appliance performance and user satisfaction. Testing conducted in simulated environments demonstrates promising results, showcasing a 12% reduction in energy costs compared to conventional energy management strategies.

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Integrated Energy Management System Approach for Off-Grid Residential Home

Falope,

Lao,

Huo

2024

Green Energy and Technology

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Real-time energy scheduling for home energy management systems with an energy storage system and electric vehicle based on a supervised-learning-based strategy

Cited by 27 publications

References 40 publications

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Reinforcement learning layout‐based optimal energy management in smart home: AI‐based approach

Integrated Energy Management System Approach for Off-Grid Residential Home

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