COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications

Published:2023-11 Issue: Volume:349 Page:121638
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Abou Houran Mohamad,Salman Bukhari Syed M.,Zafar Muhammad Hamza,Mansoor Majad,Chen Wenjie

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference56 articles.

1. Novel grass hopper optimization based MPPT of PV systems for complex partial shading conditions;Mansoor;Solar Energy,2020

2. Shading fault detection in a grid-connected PV system using vertices principal component analysis;Rouani;Renew Energy,2021

3. Cost-benefit analysis of remote hybrid wind-diesel power stations: case study Aegean Sea islands;Kaldellis;Energy Policy,2007

4. Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-wind farms power conversion systems;Zafar;Energ Conver Manage,2022

5. Performance and economic evaluation of a photovoltaic/thermal (PV/T)-cascade heat pump for combined cooling, heat and power in tropical climate area;Kong;J Energy Storage,2020

Cited by 92 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. Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation;Applied Energy;2025-01

2. Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks;Applied Energy;2024-12

3. Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data;Applied Energy;2024-12

4. Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization;Cleaner Energy Systems;2024-12

5. Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm;Energy;2024-11

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