These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

108 related articles for article (PubMed ID: 37737225)

  • 1. Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models.
    Lambert G; Hamrouche B; de Vilmarest J
    Sci Rep; 2023 Sep; 13(1):15784. PubMed ID: 37737225
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Two-Stage Multistep-Ahead Electricity Load ForecastingScheme Based on LightGBM and Attention-BiLSTM.
    Park J; Hwang E
    Sensors (Basel); 2021 Nov; 21(22):. PubMed ID: 34833791
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection.
    Krstonijević S
    Sensors (Basel); 2022 Sep; 22(19):. PubMed ID: 36236346
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France.
    Obst D; de Vilmarest J; Goude Y
    IEEE Trans Power Syst; 2021 Sep; 36(5):4754-4763. PubMed ID: 35663128
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks.
    Pavićević M; Popović T
    Sensors (Basel); 2022 Jan; 22(3):. PubMed ID: 35161797
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Forecasting next-hour electricity demand in small-scale territories: Evidence from Jordan.
    Nofal S
    Heliyon; 2023 Sep; 9(9):e19790. PubMed ID: 37809967
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies.
    Suradhaniwar S; Kar S; Durbha SS; Jagarlapudi A
    Sensors (Basel); 2021 Apr; 21(7):. PubMed ID: 33916026
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes.
    Samuel O; Alzahrani FA; Hussen Khan RJU; Farooq H; Shafiq M; Afzal MK; Javaid N
    Entropy (Basel); 2020 Jan; 22(1):. PubMed ID: 33285843
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep learning-based neural networks for day-ahead power load probability density forecasting.
    Zhou Y; Zhu D; Chen H; Guo S; Xu CY; Chang FJ
    Environ Sci Pollut Res Int; 2023 Feb; 30(7):17741-17764. PubMed ID: 36201077
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An adaptive backpropagation algorithm for long-term electricity load forecasting.
    Mohammed NA; Al-Bazi A
    Neural Comput Appl; 2022; 34(1):477-491. PubMed ID: 34393381
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A comprehensive modelling framework to forecast the demand for all hospital services.
    Ordu M; Demir E; Tofallis C
    Int J Health Plann Manage; 2019 Apr; 34(2):e1257-e1271. PubMed ID: 30901132
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network.
    Alhmoud L; Abu Khurma R; Al-Zoubi AM; Aljarah I
    Sensors (Basel); 2021 Sep; 21(18):. PubMed ID: 34577447
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Factor-Based Framework for Multivariate and Multi-step-ahead Forecasting of Large Scale Time Series.
    De Stefani J; Bontempi G
    Front Big Data; 2021; 4():690267. PubMed ID: 34568817
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Short-Term Demand Forecasting Method in Power Markets Based on the KSVM-TCN-GBRT.
    Yang G; Du S; Duan Q; Su J
    Comput Intell Neurosci; 2022; 2022():6909558. PubMed ID: 35535191
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data.
    Chung J; Jang B
    PLoS One; 2022; 17(11):e0278071. PubMed ID: 36417448
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Electricity price forecast based on the STL-TCN-NBEATS model.
    Zhang B; Song C; Jiang X; Li Y
    Heliyon; 2023 Jan; 9(1):e13029. PubMed ID: 36820190
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands.
    Butt FM; Hussain L; Mahmood A; Lone KJ
    Math Biosci Eng; 2020 Dec; 18(1):400-425. PubMed ID: 33525099
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition.
    Zhao L; Zhang X; Chen Y; Peng X; Cao Y
    Comput Intell Neurosci; 2022; 2022():1696663. PubMed ID: 36059426
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method.
    Owusu FK; Amoako-Yirenkyi P; Frempong NK; Omari-Sasu AY; Mensah IA; Martin H; Sakyi A
    Heliyon; 2023 Aug; 9(8):e18821. PubMed ID: 37636468
    [TBL] [Abstract][Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 6.