Machine Learning for Energy Load Prediction and its Interpretation

被引:0
作者
Charytanowicz, Malgorzata [1 ]
Olwert, Anna [2 ]
Radziszewska, Weronika [3 ]
Jarnicka, Jolanta [3 ]
Gajowniczek, Krzysztof [4 ]
Zabkowski, Tomasz [4 ]
Brozyna, Jacek [5 ]
Mentel, Grzegorz [5 ]
Matejko, Grzegorz [6 ]
机构
[1] Research Institute Pas, Center for Methods of Data Analysis Systems, Warsaw, Poland
[2] Research Institute Pas, Department of Computer Science Systems, Warsaw, Poland
[3] Research Institute Pas, Center for Computer Modelling Systems, Warsaw, Poland
[4] Warsaw University of Life Sciences, Institute of Information Technology, Warsaw, Poland
[5] Rzeszow University of Technology, Department of Quantitative Methods, Rzeszów, Poland
[6] Polskie Towarzystwo Cyfrowe, Lublin, Poland
来源
2022 IEEE 11th International Conference on Intelligent Systems, IS 2022 | 2022年
关键词
Compilation and indexing terms; Copyright 2024 Elsevier Inc;
D O I
11th IEEE International Conference on Intelligent Systems, IS 2022
中图分类号
学科分类号
摘要
Adaptive boosting - Additives - Electric load forecasting - Electric power plant loads - Machine learning - Regression analysis
引用
收藏
相关论文
共 50 条
  • [41] Patient-specific quality assurance prediction models based on machine learning for novel dual-layered MLC linac
    Zhu, Heling
    Zhu, Qizhen
    Wang, Zhiqun
    Yang, Bo
    Zhang, Wenjun
    Qiu, Jie
    Medical Physics, 2023, 50 (02): : 1205 - 1214
  • [42] Disease prediction using deep learning
    Bhatia, Gresha
    Bhat, Shravan
    Choudhary, Vivek
    Deopurkar, Aditya
    Talreja, Sahil
    2021 2nd International Conference for Emerging Technology, INCET 2021, 2021,
  • [43] Federated Learning for Non-intrusive Load Monitoring
    Meng, Zhaorui
    Xie, Xiaozhu
    Xie, Yanqi
    IAENG International Journal of Applied Mathematics, 2023, 53 (03)
  • [44] The Next Generation of Drugs Will Be Enhanced by Machine Learning
    Wishart, Grant
    Lanza, Guido
    Genetic Engineering and Biotechnology News, 2023, 43 (04): : 16 - 17
  • [45] Machine learning spectral functions in lattice QCD
    Chen, S.-Y.
    Ding, H.-T.
    Liu, F.-Y.
    Papp, G.
    Yang, C.-B.
    arXiv, 2021,
  • [46] Machine learning models for classification of BGP anomalies
    Al-Rousan, Nabil M.
    Trajkovic, Ljiljana
    2012 IEEE 13th International Conference on High Performance Switching and Routing, HPSR 2012, 2012, : 103 - 108
  • [47] An analysis of course evaluation questionnaire by machine learning
    Dept. of Computer Science, National Defense Academy of Japan, 1-10-20 Hashirimizu, Yokosuka, Kanagawa, Japan
    AIP Conf. Proc.,
  • [48] Leveraging machine learning in the innovation of functional materials
    Sun, Zhehao
    Yin, Hang
    Yin, Zongyou
    MATTER, 2023, 6 (08) : 2553 - 2555
  • [49] Machine Learning for Cybersecurity Frameworks in Smart Farming
    Eleftheriadis, Charis
    Andronikidis, Georgios
    Kyranou, Konstantinos
    Pechlivani, Eleftheria Maria
    Hadjigeorgiou, Ioannis
    Batzos, Zisis
    2024 28th International Conference on Information Technology, IT 2024, 2024,
  • [50] Determining Research Priorities Using Machine Learning
    Thomas, Brian A.
    Thronson, Harley
    Buonomo, Anthony
    Barbier, Louis
    arXiv,