Improved convolutional neural network‐based quantile regression for regional photovoltaic generation probabilistic forecast-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Improved convolutional neural network‐based quantile regression for regional photovoltaic generation probabilistic forecast

Published:2020-09-24 Issue:14 Volume:14 Page:2712-2719
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Generation

Author:

Yu Yixiao¹,Wang Mengxia¹,Yan Fangqing²,Yang Ming¹^ORCID,Yang Jiajun³

Affiliation:

1. Key Laboratory of Power System Intelligent Dispatch and ControlShandong UniversityJinan250061People's Republic of China

2. Laiwu Electric Power Supply Company of State Grid Shandong Electric Power CompanyLaiwu271100People's Republic of China

3. State Grid Shaanxi Electric Power Research InstituteXi'an710054People's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2019.0949

Reference33 articles.

1. Determination method of insolation prediction with fuzzy and applying neural network for long‐term ahead PV power output correction;Yona A.;IEEE Trans. Sustain. Energy,2013

2. Post‐processing techniques and principal component analysis for regional wind power and solar irradiance forecasting;Davò F.;Sol. Energy,2016

3. A solar time based analog ensemble method for regional solar power forecasting;Zhang X.;IEEE Trans. Sustain. Energy,2019

4. Regional forecasts of photovoltaic power generation according to different data availability scenarios: a study of four methods;Junior J.G.D.S.F.;Prog. Photovolt. Res. Appl.,2015

5. Data‐driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data;Pierro M.;Sol. Energy,2017

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A method for accurate prediction of photovoltaic power based on multi-objective optimization and data integration strategy;Applied Mathematical Modelling;2024-12

2. A spatial–temporal data-driven deep learning framework for enhancing ultra-short-term prediction of distributed photovoltaic power generation;International Journal of Electrical Power & Energy Systems;2024-09

3. Interval prediction of short‐term photovoltaic power based on an improved GRU model;Energy Science & Engineering;2024-07

4. Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data;Applied Energy;2024-05

5. Photovoltaic Region Prediction Based on Improved Convolutional Neural Network and Cluster Analysis;2024 7th International Conference on Energy, Electrical and Power Engineering (CEEPE);2024-04-26

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3