Deep learning for time series forecasting: The electric load case
Author:
Affiliation:
1. Faculty of Informatics Università della Svizzera Italiana Lugano Switzerland
2. Department of Electronics, Information, and Bioengineering Politecnico di Milano Milan Italy
Publisher
Institution of Engineering and Technology (IET)
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Information Systems
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/cit2.12060
Reference101 articles.
1. Smart grid – the new and improved power grid: a survey;Fang X.;IEEE Commun. Surv. Tutorials,2012
2. A methodology for electric power load forecasting;Almeshaiei E.;Alexandria Eng. J.,2011
3. Neural networks for short‐term load forecasting: a review and evaluation;Hippert H.S.;IEEE Trans Power Syst,2001
4. Analysis of an adaptive time‐series autoregressive moving‐average (ARMA) model for short‐term load forecasting;Chen J.‐F.;Elec. Power Syst. Res,1995
5. Short‐term load forecasting via arma model identification including non‐Gaussian process considerations;Huang S.‐J.;IEEE Trans. Power Syst,2003
Cited by 98 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. Optimal demand response based dynamic pricing strategy via Multi-Agent Federated Twin Delayed Deep Deterministic policy gradient algorithm;Engineering Applications of Artificial Intelligence;2024-07
2. Highly fluctuating short-term load forecasting based on improved secondary decomposition and optimized VMD;Sustainable Energy, Grids and Networks;2024-03
3. Effects of CO2 concentration and time on algal biomass film, NO3–N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II;Journal of Environmental Management;2024-02
4. Vehicle type classification in intelligent transportation systems using deep learning;Journal of Intelligent & Fuzzy Systems;2024-01-08
5. Towards better transition modeling in recurrent neural networks: The case of sign language tokenization;Neurocomputing;2024-01
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