Concept Drift Scenarios in Electrical Load Forecasting with Different Generation Modalities-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Concept Drift Scenarios in Electrical Load Forecasting with Different Generation Modalities

Published:2022-12-01 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Future Trends in Smart Communities (ICFTSC)
language:
Short-container-title:

Author:

Azeem Abdul¹,Ismail Idris¹,Jameel Syed Muslim²,Romlie Fakhizan¹,Danyaro Kamaluddeen Usman³

Affiliation:

1. Universiti Teknologi PETRONAS,Electrical and Electronic Engineering Department,Perak,Malaysia

2. National University of Ireland,Structure Lab, School of Engineering,Galway,Ireland

3. Universiti Teknologi PETRONAS,Computer Science Department,Perak,Malaysia

Funder

PETRONAS

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10038980/10039723/10039888.pdf?arnumber=10039888

Reference21 articles.

1. Online Ensemble Learning for Load Forecasting

2. Scenario prediction for power loads using a pixel convolutional neural network and an optimization strategy

3. An adaptive hybrid fractal model for short-term load forecasting in power systems

4. Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

5. Short term load forecasting based on feature extraction and improved general regression neural network model

Cited by 1 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. Mitigating Concept-Drift Challenges in Evolving Smart-Grids: An Adaptive Ensemble-Lstm for Enhanced Load Forecasting;2024

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