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Short-Term Load Forecasting Based on Deep Learning for End-User Transformer Subject to Volatile Electric Heating Loads

  • Published:2019 Issue: Volume:7 Page:162697-162707
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Chen QifangORCID,Xia MingchaoORCID,Lu Teng,Jiang Xichen,Liu Wenxia,Sun Qinfei

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

State Grid Corporation Science and Technology Project

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

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. Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction;Chemical Product and Process Modeling;2024-07-01

2. RLIDT: A Novel Reinforcement Learning-Infused Deep Transformer Model for Multivariate Electricity Load Forecasting;2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2024-06-27

3. Hybrid machine learning application with integration of meta-heuristic algorithm for prediction of cooling load;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-05-17

4. Heating load prediction in buildings using decision tree machine learning method;Journal of Intelligent & Fuzzy Systems;2024-04-25

5. Research on cooling load estimation through optimal hybrid models based on Naive Bayes;Journal of Engineering and Applied Science;2024-03-20
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