A VMD-deep learning approach for individual load monitoring and forecasting for residential buildings energy management-Reference-Cited by-同舟云学术

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A VMD-deep learning approach for individual load monitoring and forecasting for residential buildings energy management

Published:2024-06 Issue: Volume:8 Page:100624
ISSN:2772-6711
Container-title:e-Prime - Advances in Electrical Engineering, Electronics and Energy
language:en
Short-container-title:e-Prime - Advances in Electrical Engineering, Electronics and Energy

Author:

Jrhilifa Ismael^ORCID,Ouadi Hamid,Jilbab Abdelilah,Mounir Nada^ORCID,Ouaguid Abdellah

Publisher

Elsevier BV

Reference38 articles.

1. Forecasting smart home electricity consumption using VMD-Bi-GRU;Jrhilifa;Energy Efficiency,2024

2. Q-learning- based optimization of smart home's wireless sensors network lifetime;Jrhilifa;Int. J. Renew. Energy Res.,2023

3. Smart home's wireless sensor networks lifetime optimizing using q-learning;Jrhilifa,2021

4. Residential electricity conservation in response to auto-generated, multi-featured, personalized eco-feedback designed for large scale applications with utilities;Meinrenken;Energy Build.,2021

5. Non-intrusive load monitoring through home energy management systems: a comprehensive review;Hosseini;Renew. Sustain. Energy Rev.,2017

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code;Energy;2024-10

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