Individual household demand response potential evaluation and identification based on machine learning algorithms-Reference-Cited by-同舟云学术

Individual household demand response potential evaluation and identification based on machine learning algorithms

Published:2023-03 Issue: Volume:266 Page:126505
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Shi Renwei,Jiao Zaibin

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference53 articles.

1. Brady Stoll. Demand response for variable renewable energy integration: a proposed approach and its impacts;McPherson;Energy,2020

2. Optimisation of demand response in electric power systems, a review;Rezaee Jordehi;Renew Sustain Energy Rev,2019

3. Ioana pisica, gareth A. Taylor, kevin J. Munisami. Residential demand response strategies and applications in active distribution network management;Davarzani;Renew Sustain Energy Rev,2021

4. Demand response performance and uncertainty: a systematic literature review;Silva;Energy Strategy Rev,2022

5. Natarajan Prabaharan. Demand side management in microgrid: a critical review of key issues and recent trends;Kanakadhurga;Renew Sustain Energy Rev,2022

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