BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

122 related articles for article (PubMed ID: 38502675)

  • 1. Short-term power load forecasting method based on Bagging-stochastic configuration networks.
    Pang X; Sun W; Li H; Liu W; Luan C
    PLoS One; 2024; 19(3):e0300229. PubMed ID: 38502675
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network.
    Sun L; Qin H; Przystupa K; Majka M; Kochan O
    Sensors (Basel); 2022 Oct; 22(20):. PubMed ID: 36298250
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm.
    Pang X; Sun W; Li H; Wang Y; Luan C
    PeerJ Comput Sci; 2022; 8():e1108. PubMed ID: 36262153
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multi-model fusion short-term power load forecasting based on improved WOA optimization.
    Ji X; Liu D; Xiong P
    Math Biosci Eng; 2022 Sep; 19(12):13399-13420. PubMed ID: 36654052
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Ultra short term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi head attention mechanism.
    Gou Y; Guo C; Qin R
    PLoS One; 2024; 19(3):e0299632. PubMed ID: 38517854
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Research on Impulse Power Load Forecasting Based on Improved Recurrent Neural Networks.
    Feng C; Xu K; Ma H
    Comput Intell Neurosci; 2022; 2022():2784563. PubMed ID: 35502351
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improved Neural Networks with Random Weights for Short-Term Load Forecasting.
    Lang K; Zhang M; Yuan Y
    PLoS One; 2015; 10(12):e0143175. PubMed ID: 26629825
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Load forecasting method based on CEEMDAN and TCN-LSTM.
    Heng L; Hao C; Nan LC
    PLoS One; 2024; 19(7):e0300496. PubMed ID: 38968242
    [TBL] [Abstract][Full Text] [Related]  

  • 9. The Short-Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China.
    Dong H; Gao Y; Fang Y; Liu M; Kong Y
    Comput Intell Neurosci; 2021; 2021():3693294. PubMed ID: 34567100
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Short-term load forecasting system based on sliding fuzzy granulation and equilibrium optimizer.
    Li S; Wang J; Zhang H; Liang Y
    Appl Intell (Dordr); 2023 Jun; ():1-35. PubMed ID: 37363386
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network.
    Ozcan A; Catal C; Kasif A
    Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770422
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet.
    Liang L; Cui J; Zhao J; Qiang Y; Zhao J
    Math Biosci Eng; 2024 Feb; 21(2):3391-3421. PubMed ID: 38454733
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting.
    Yang S; Yuan A; Yu Z
    Environ Sci Pollut Res Int; 2023 Jan; 30(5):11689-11705. PubMed ID: 36098919
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition.
    Tang J; Chien YR
    Sensors (Basel); 2022 Sep; 22(19):. PubMed ID: 36236512
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.
    Zhang X; Zhang Q; Zhang G; Nie Z; Gui Z; Que H
    Int J Environ Res Public Health; 2018 May; 15(5):. PubMed ID: 29883381
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks.
    Mahjoub S; Chrifi-Alaoui L; Marhic B; Delahoche L
    Sensors (Basel); 2022 May; 22(11):. PubMed ID: 35684681
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China.
    Sun J; Dong H; Gao Y; Fang Y; Kong Y
    Comput Intell Neurosci; 2021; 2021():1502932. PubMed ID: 34745245
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM.
    Alsharekh MF; Habib S; Dewi DA; Albattah W; Islam M; Albahli S
    Sensors (Basel); 2022 Sep; 22(18):. PubMed ID: 36146256
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.