Load Forecasting Considering Demand Response Mechanism-Reference-Cited by-同舟云学术

Load Forecasting Considering Demand Response Mechanism

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 Panda Forum on Power and Energy (PandaFPE)
language:
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Author:

Zhang Qizhe¹,Gao Yi²,Mo Wenhao¹,Han Fujia¹,Zhang Liang²

Affiliation:

1. China Electric Power Research Institute,Artificial Intelligence Application Research Department,Beijing,China

2. Research Institute of SGCC,Tianjin Power Economic,Tianjin,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10140170/10140192/10140881.pdf?arnumber=10140881

Reference22 articles.

1. RBF-NN short-term load forecasting model considering integrated demand response influencing factors[J];zhisheng;Chinese Journal of Electrical Engineering,2018

2. From controllable loads to generalized demand-side resources: a review on developments of demand-side resources[J];b;Renewable and Sustainable Energy Reviews,2016

3. Research on Elman-NN short-term load forecasting model with demand response [J];daolin;IEE New Power Electronic Techniques,2017

4. Short-term load forecasting for power systems accounting for demand response and deep structural multi-task learning [J];ma;Electrical measurement and Instrumentation,2019

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