LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences

Author:

Kim Nakyoung,Kim Minkyung,Choi Jun Kyun

Publisher

IEEE

Cited by 42 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. Advancing industrial building energy measurement and verification (M&V) with deep learning: Evaluating data size and feature selection impact;Energy and Buildings;2024-09

2. Data pipeline for real-time energy consumption data management and prediction;Frontiers in Big Data;2024-03-13

3. Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review;International Journal of Technology;2024-01-25

4. Learning With Correlation-Guided Attention for Multienergy Consumption Forecasting;IEEE Transactions on Industrial Informatics;2024

5. Electricity consumption prediction based on Transformer-LSTM;2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC);2023-12-22

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