On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems-Reference-Cited by-同舟云学术

On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Author:

Wang Jiawei¹^ORCID,Pinson Pierre²^ORCID,Chatzivasileiadis Spyros¹,Panteli Mathaios³^ORCID,Strbac Goran⁴^ORCID,Terzija Vladimir⁵

Affiliation:

1. Department of Wind and Energy Systems, Technical University of Denmark, Lyngby, Denmark

2. Dyson School of Design Engineering, Imperial College London, London, U.K.

3. Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

4. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.

5. Center for Energy Science and Technology, Skolkovo Institute of Science and Technology, Moscow, Russia

Funder

ERC Starting Grant VeriPhIED

H2020 EUniversal Project

Skoltech and The Ministry of Education and Science of Russian Federation

VILLUM FONDEN

Data-driven Approaches for enhancing resilience of Multi-Energy systems

EPSRC Project: NetworkPlus - A green, connected and prosperous Britain

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Renewable Energy, Sustainability and the Environment

Link

Reference103 articles.

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