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 *

240 related articles for article (PubMed ID: 36366098)

  • 1. A Multi-Stage Planning Method for Distribution Networks Based on ARIMA with Error Gradient Sampling for Source-Load Prediction.
    Yan S; Hu M
    Sensors (Basel); 2022 Nov; 22(21):. PubMed ID: 36366098
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources.
    R Singh A; Kumar RS; Bajaj M; Khadse CB; Zaitsev I
    Sci Rep; 2024 Aug; 14(1):19207. PubMed ID: 39160194
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Active power control strategy for wind farms based on power prediction errors distribution considering regional data.
    Kader MS; Mahmudh R; Xiaoqing H; Niaz A; Shoukat MU
    PLoS One; 2022; 17(8):e0273257. PubMed ID: 36001548
    [TBL] [Abstract][Full Text] [Related]  

  • 4. An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting.
    Li G; Yu L; Zhang Y; Sun P; Li R; Zhang Y; Li G; Wang P
    Environ Sci Pollut Res Int; 2023 Mar; 30(14):41937-41953. PubMed ID: 36640232
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Optimization scheme of wind energy prediction based on artificial intelligence.
    Zhang Y; Li R; Zhang J
    Environ Sci Pollut Res Int; 2021 Aug; 28(29):39966-39981. PubMed ID: 33763837
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Demand for flexibility improvement of thermal power units and accommodation of wind power under the situation of high-proportion renewable integration-taking North Hebei as an example.
    Luo G; Zhang X; Liu S; Dan E; Guo Y
    Environ Sci Pollut Res Int; 2019 Mar; 26(7):7033-7047. PubMed ID: 30644051
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Smart Distribution Boards (Smart DB), Non-Intrusive Load Monitoring (NILM) for Load Device Appliance Signature Identification and Smart Sockets for Grid Demand Management.
    Kerk SG; Hassan NU; Yuen C
    Sensors (Basel); 2020 May; 20(10):. PubMed ID: 32443817
    [TBL] [Abstract][Full Text] [Related]  

  • 8. EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation.
    Ran M; Huang J; Qian W; Zou T; Ji C
    Heliyon; 2023 Jul; 9(7):e18053. PubMed ID: 37496909
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.
    Zhang R; Song H; Chen Q; Wang Y; Wang S; Li Y
    PLoS One; 2022; 17(1):e0262009. PubMed ID: 35030203
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction.
    Zhang Y; Chen Y
    Environ Sci Pollut Res Int; 2022 Mar; 29(15):22661-22674. PubMed ID: 34797536
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems.
    Amir M; Zaheeruddin ; Haque A
    Sci Prog; 2022; 105(4):368504221132144. PubMed ID: 36263519
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.
    Quan H; Srinivasan D; Khosravi A
    IEEE Trans Neural Netw Learn Syst; 2015 Sep; 26(9):2123-35. PubMed ID: 25532191
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models.
    Elseify MA; Hashim FA; Hussien AG; Abdel-Mawgoud H; Kamel S
    Sci Rep; 2024 Jun; 14(1):14173. PubMed ID: 38898067
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.
    Adeyinka DA; Muhajarine N
    BMC Med Res Methodol; 2020 Dec; 20(1):292. PubMed ID: 33267817
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Cluster Analysis and Model Comparison Using Smart Meter Data.
    Shaukat MA; Shaukat HR; Qadir Z; Munawar HS; Kouzani AZ; Mahmud MAP
    Sensors (Basel); 2021 May; 21(9):. PubMed ID: 34063197
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Adaptive grid based multi-objective Cauchy differential evolution for stochastic dynamic economic emission dispatch with wind power uncertainty.
    Zhang H; Lei X; Wang C; Yue D; Xie X
    PLoS One; 2017; 12(9):e0185454. PubMed ID: 28961262
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Deep learning-driven hybrid model for short-term load forecasting and smart grid information management.
    Wen X; Liao J; Niu Q; Shen N; Bao Y
    Sci Rep; 2024 Jun; 14(1):13720. PubMed ID: 38877081
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm.
    Xu R; Lin F; Shao W; Wang H; Meng F; Li J
    Entropy (Basel); 2024 Apr; 26(4):. PubMed ID: 38667885
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction.
    Wang Y; Wu Y; Xu H; Chen Z; Gao J; Xu Z; Li L
    Network; 2023; 34(3):151-173. PubMed ID: 37246622
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.