An Automated Approach for Screening Residential PV Applications Using a Random Forest Model-Reference-Cited by-同舟云学术

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An Automated Approach for Screening Residential PV Applications Using a Random Forest Model

Published:2023 Issue: Volume:10 Page:327-334
ISSN:2687-7910
Container-title:IEEE Open Access Journal of Power and Energy
language:
Short-container-title:IEEE Open J. Power Energy

Author:

Wang Wenbo¹^ORCID,Keen Jeremy²,Bank Jason²,Giraldez Julieta³,Montano-Martinez Karen⁴^ORCID

Affiliation:

1. Grid Planning and Analysis Center, National Renewable Energy Laboratory, Golden, CO, USA

2. Electrical Distribution Design, National Information Solutions Cooperative, Lake Saint Louis, MO, USA

3. Customer Solutions, Kevala Inc., San Francisco, CA, USA

4. Distribution Operations and Planning, Electric Power Research Institute, Knoxville, TN, USA

Funder

National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy

Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

https://ieeexplore.ieee.org/ielam/8784343/9999142/10108056-aam.pdf

Reference24 articles.

1. Distribution System Parameter and Topology Estimation Applied to Resolve Low-Voltage Circuits on Three Real Distribution Feeders

2. Identification of power distribution network topology via voltage correlation analysis

3. Fast Probabilistic Hosting Capacity Analysis for Active Distribution Systems

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1. Classification of anomalies in photovoltaic systems using supervised machine learning techniques and real data;Energy Reports;2024-06

2. A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks;IEEE Access;2024

3. Investigating the interaction parameters on ventilation supercavitation phenomena: Experimental and numerical analysis with machine learning interpretation;Physics of Fluids;2023-11-01

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