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AuthorAmer, Aya
AuthorShaban, Khaled
AuthorMassoud, Ahmed
Available date2022-12-21T10:01:46Z
Publication Date2022
Publication NameIEEE Transactions on Smart Grid
ResourceScopus
URIhttp://dx.doi.org/10.1109/TSG.2022.3198401
URIhttp://hdl.handle.net/10576/37503
AbstractWith the smart grid and smart homes development, different data are made available, providing a source for training algorithms, such as deep reinforcement learning (DRL), in smart grid applications. These algorithms allowed the home energy management systems (HEMSs) to deal with the computational complexities and the uncertainties at the end-user side. This article proposes a multi-objective DRL-HEMS: a data-driven solution, which is a trained DRL agent in a HEMS to optimize the energy consumption of a household with different appliances, an energy storage system, a photovoltaic system, and an electric vehicle. The proposed solution reduces the electricity cost considering the resident’s comfort level and the loading level of the distribution transformer. The distribution transformer load is optimized by optimizing its loss-of-life. The performance of DRL-HEMS is evaluated using real-world data, and results show that it can optimize multiple appliances operation, reduce electricity bill cost, dissatisfaction cost, and the transformer loading condition. IEEE
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCosts
Deep reinforcement learning
demand response
Home appliances
home energy management
Load modeling
multi-objective deep reinforcement learning
Optimization
Reinforcement learning
Schedules
Transformers
TitleDRL-HEMS: Deep Reinforcement Learning Agent for Demand Response in Home Energy Management Systems Considering Customers and Operators Perspectives
TypeArticle
Pagination1-Jan
dc.accessType Abstract Only


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