Alleviating distribution shift and mining hidden temporal variations for ultra-short-term wind power forecasting-Reference-Cited by-同舟云学术

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Alleviating distribution shift and mining hidden temporal variations for ultra-short-term wind power forecasting

Published:2024-03 Issue: Volume:290 Page:130077
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Wei Haochong,Chen Yan,Yu Miaolin,Ban Guihua,Xiong Zhenhua,Su Jin,Zhuo Yixin,Hu Jiaqiu

Funder

Natural Science Foundation of Guangxi Province

Publisher

Elsevier BV

Subject

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

Reference56 articles.

1. Approaches to wind power curve modeling: a review and discussion;Wang;Renew Sustain Energy Rev,2019

2. Feasibility study of offshore wind turbines with hybrid monopile foundation based on centrifuge modeling;Wang;Appl Energy,2018

3. Investigation of energy output in mountain wind farm using multiple-units SCADA data;Dai;Appl Energy,2019

4. Offshore wind resource in the context of global climate change over a tropical area;Vu Dinh;Appl Energy,2022

5. Sparse heteroscedastic multiple Spline regression models for wind turbine power curve modeling;Wang;IEEE Trans Sustain Energy,2021

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