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المؤلفAmer, Aya
المؤلفShaban, Khaled
المؤلفMassoud, Ahmed
تاريخ الإتاحة2022-12-21T10:01:46Z
تاريخ النشر2022
اسم المنشورIEEE Transactions on Smart Grid
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TSG.2022.3198401
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37503
الملخصWith 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
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعCosts
Deep reinforcement learning
demand response
Home appliances
home energy management
Load modeling
multi-objective deep reinforcement learning
Optimization
Reinforcement learning
Schedules
Transformers
العنوانDRL-HEMS: Deep Reinforcement Learning Agent for Demand Response in Home Energy Management Systems Considering Customers and Operators Perspectives
النوعArticle
الصفحات1-Jan
dc.accessType Abstract Only


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