Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives-Reference-Cited by-同舟云学术

Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

Published:2022-11 Issue: Volume:10 Page:100195
ISSN:2666-5468
Container-title:Energy and AI
language:en
Short-container-title:Energy and AI

Author:

Liu Zhengxuan,Sun Ying,Xing Chaojie,Liu Jia,He Yingdong,Zhou Yuekuan,Zhang Guoqiang

Publisher

Elsevier BV

Subject

Artificial Intelligence,General Energy,Engineering (miscellaneous)

Reference171 articles.

1. Potential challenges of integrating large-scale wind energy into the power grid–a review;Shafiullah;Renew Sustain Energy Rev,2013

2. Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optimisations: a state-of-the-art review;Zhou;Renew Sustain Energy Rev,2020

3. Artificial neural network-based smart aerogel glazing in low-energy buildings: a state-of-the-art review;Zhou;iScience,2021

4. Energetics systems and artificial intelligence: applications of industry 4.0;Ahmad;Energy Rep,2022

5. Multi-objective optimisation of an interactive buildings-vehicles energy sharing network with high energy flexibility using the Pareto archive NSGA-II algorithm;Zhou;Energy Convers Manage,2020

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