Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation-Reference-Cited by-同舟云学术

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Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation

Published:2021-12 Issue: Volume:201 Page:107551
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Zhang Wanying,He Yaoyao^ORCID,Yang Shanlin

Funder

National Natural Science Foundation of China

Anhui Provincial Natural Science Foundation

Fundamental Research Funds for the Central Universities

Natural Science Foundation for Distinguished Young Scholars of Anhui Province

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference55 articles.

1. Convolutional and recurrent neural network based model for short-term load forecasting;Eskandari;Electr. Power Syst. Res.,2021

2. Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest;He;Appl. Energy,2020

3. Probabilistic load forecasting via quantile regression averaging on sister forecasts;Liu;IEEE Trans. Smart Grid,2017

4. A multivariate approach to probabilistic industrial load forecasting;Bracale;Electr. Power Syst. Res.,2020

5. Deep learning architecture for direct probability density prediction of small-scale solar generation;Afrasiabi;IET Gener. Transm. Distrib.,2019

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