A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs-Reference-Cited by-同舟云学术

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A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs

Published:2024-05 Issue: Volume:361 Page:122943
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Michalakopoulos Vasilis^ORCID,Sarmas Elissaios^ORCID,Papias Ioannis^ORCID,Skaloumpakas Panagiotis,Marinakis Vangelis,Doukas Haris

Publisher

Elsevier BV

Reference51 articles.

1. ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands;Sarmas;Sustainable Cities Soc,2022

2. Demand response in electricity markets: An overview;Albadi,2007

3. Bahrami Sh, Parniani M, Vafaeimehr A. A modified approach for residential load scheduling using smart meters. In: IEEE PES innovative smart grid technologies conference Europe. 2012.

4. Optimal demand response aggregation in wholesale electricity markets;Parvania;IEEE Trans Smart Grid,2013

5. Residential demand response: Dynamic energy management and time-varying electricity pricing;Muratori;IEEE Trans Power Syst,2016

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