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• W4385648754 abstract "The increasing popularity of home automation and the rising global electricity costs have emphasized the importance of energy conservation for consumers. With smart meters, machine learning models can anticipate equipment behavior by monitoring and recording residential power use. Multi-Energy Management Systems, which allow smart grid flexibility, have garnered interest. Smart meters and smart energy gadgets in homes require autonomous multi-energy management systems. These systems should efficiently utilize real-time data to plan device consumption, reducing costs for end users. The model incorporates two Long Short-Term Memory networks, capturing short-term and long-term dependencies in energy consumption patterns. This enables the Multi-Energy Management Systems to make accurate predictions and manage energy resources in real-time. The primary objectives are to minimize reliance on the grid and maximize the utilization of renewable energy sources. The proposed Deep Dual- Long Short-Term Memory model achieves impressive accuracy rates, with scores ranging from 97% to 99% for recall, F1-score, and precision. Numerical findings demonstrate the superior performance of the proposed method compared to existing approaches, showcasing its ability to lower energy consumption and meet operational constraints. The results indicate that the proposed strategy optimizes energy use, providing cost savings and satisfying user requirements." @default.
• W4385648754 created "2023-08-09" @default.
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• W4385648754 date "2023-11-01" @default.
• W4385648754 modified "2023-09-23" @default.
• W4385648754 title "MEMS: An automated Multi-Energy Management System for smart Residences Using the DD-LSTM approach" @default.
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• W4385648754 doi "https://doi.org/10.1016/j.scs.2023.104850" @default.
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