Empowering Industrial Energy Management: Advancing Short-Term Load Forecasting with LSTM and CNN Deep Learning Models - Insights from a Moroccan Case Study-Reference-Cited by-同舟云学术

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Empowering Industrial Energy Management: Advancing Short-Term Load Forecasting with LSTM and CNN Deep Learning Models - Insights from a Moroccan Case Study

Published:2024-04-25 Issue: Volume:129 Page:1-6
ISSN:
Container-title:2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)
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Author:

Boumais Khaoula¹,Messaoudi Fayçal¹,Lagnaoui Saloua²,Fallah Saad El³,Udris Dainius⁴

Affiliation:

1. ENSA, Sidi Mohammed ben Abdallah University,AI, Data Science and Emerging Systems Laboratory,Fes,Morocco

2. ENSA, Sidi Mohamed Ben Abdellah University,Engineering, Systems and Applications Laboratory,Fes,Morocco

3. Private University of Fez (UPF),Systems and Sustainable Environment Laboratory,Fes,Morocco

4. Vilnius Gediminas Technical University,Faculty of Electronics,Department of Electrical Engineering,Vilnius,Lithuania

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10542546/10542531/10542574.pdf?arnumber=10542574

Reference20 articles.

1. Climate agreement and technology diffusion: Impact of the Kyoto Protocol on international patent applications for renewable energy technologies;Miyamoto;Energy Policy,2019

2. Climate change 2014: synthesis report,2014

3. Self-consumption of electricity produced from PV systems in apartment buildings - Comparison of the situation in Australia, Austria, Denmark, Germany, Greece, Italy, Spain, Switzerland and the USA;Jager-Waldau

4. A review of renewable energy sources, sustainability issues and climate change mitigation;Owusu;Cogent Eng.,2016

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