Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks

Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks

Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessi...

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Bibliographic Details
Main Authors: Zain Anwer Memon, Riccardo Trinchero, Paolo Manfredi, Flavio Canavero, Igor S. Stievano
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Energies
Subjects:
high-dimensional problems
least-squares support vector machine
polynomial chaos expansion
power distribution network
power-flow analysis
principal component analysis
Online Access:https://www.mdpi.com/1996-1073/13/18/4881

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https://www.mdpi.com/1996-1073/13/18/4881

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