Machine learning algorithm for activity‐aware demand response considering energy savings and comfort requirements-Reference-Cited by-同舟云学术

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Machine learning algorithm for activity‐aware demand response considering energy savings and comfort requirements

Published:2020-10 Issue:5 Volume:3 Page:730-737
ISSN:2515-2947
Container-title:IET Smart Grid
language:en
Short-container-title:IET Smart Grid

Author:

Zhang Yue¹^ORCID,Srivastava Anurag. K.¹^ORCID,Cook Diane¹

Affiliation:

1. School of Electrical Engineering and and Computer ScienceWashington State UniversityPullmanWA99164USA

Funder

National Science Foundation

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-stg.2019.0249

Reference50 articles.

1. U.S. Energy Information Administration: ‘Residential energy consumption survey’ 2017. Available atwww.eia.gov

2. Robust scheduling of smart appliances with uncertain electricity prices in a heterogeneous population;Bassamzadeh N.;Energy Build.,2014

3. Bi‐level optimized dispatch strategy of electric supply‐demand balance considering risk‐benefit coordination;Xu Q.;IET Smart Grid,2018

4. Demand response in China: regulations, pilot projects and recommendations‐A review;Dong J.;Renew. Sustain. Energy Rev.,2016

5. Demand response for sustainable energy systems: a review, application and implementation strategy;Shariatzadeh F.;Renew. Sust. Energy Rev.,2015

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