Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques-Reference-Cited by-同舟云学术

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

Published:2023 Issue: Volume:11 Page:19469-19486
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jaramillo Andres F. Moreno¹^ORCID,Lopez-Lorente Javier²^ORCID,Laverty David M.¹^ORCID,Brogan Paul V.³,Velasquez Santiago H. Hoyos⁴,Martinez-Del-Rincón Jesus¹^ORCID,Foley Aoife M.⁵^ORCID

Affiliation:

1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, U.K

2. Department of Electrical and Computer Engineering, FOSS Research Centre for Sustainable Energy, PV Technology Laboratory, University of Cyprus, Nicosia, Cyprus

3. Data Applications, Phasora Ltd., Belfast, U.K

4. EnergEIA Research Cluster, EIA University, Envigado, Antioquia, Colombia

5. School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, U.K

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/6287639/10005208/10049841-aam.pdf

Reference43 articles.

1. Doubling the ADMD in housing schemes to cater for future electrification of heat and transport

2. Non-Intrusive Load Monitoring Algorithm for PV Identification in the Residential Sector

3. Identification of Distributed Energy Resources in Low Voltage Distribution Networks

4. Cross Validation Voting for Improving CNN Classification in Grocery Products

5. Techno-Economic Assessment of Grid-Level Battery Energy Storage Supporting Distributed Photovoltaic Power

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