AI-powered Prediction Drives Hungarian Renewable Energy Integration-Reference-Cited by-同舟云学术

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AI-powered Prediction Drives Hungarian Renewable Energy Integration

Published:2024-06-04 Issue: Volume: Page:641-646
ISSN:
Container-title:2024 6th Global Power, Energy and Communication Conference (GPECOM)
language:
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Author:

Zolfaghari Farzad¹,Alharasees Omar²,Kale Utku²

Affiliation:

1. Technical University of Košice,Faculty of Aeronautics,Department of Aviation Engineering,Kosice,Slovakia

2. Budapest University of Technology and Economics,Faculty of Transportation Engineering and Vehicle Engineering,Department of Aeronautics and Naval Architecture,Budapest,Hungary

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10582548/10582512/10582633.pdf?arnumber=10582633

Reference19 articles.

1. Fit for 55,2023

2. Solutions to integrate high shares of variable renewable energy (Report to the G20 Energy Transitions Working Group (ETWG)),2019

3. Energy Systems In Mitigation of Climate Change;Bruckner,2014

4. Sustainable energy planning: Leapfrogging the energy poverty gap in Africa

5. A review of international experience in forecasting renewable energy generation using machine learning methods

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