Author: admin

Continue Reading

  • Saint Mary's 78-57 Portland (Jan 2, 2026) Game Recap – ESPN

    1. Saint Mary’s 78-57 Portland (Jan 2, 2026) Game Recap  ESPN
    2. Saint Mary’s (CA) hosts Foxwell and Portland  The Washington Post
    3. Saint Mary’s (CA) vs. Portland Dunkel Predictions & Vegas Odds – Jan. 2  The Dunkel Index
    4. Portland vs Saint Mary’s…

    Continue Reading

  • KKR confirms exclusion of Mustafizur Rahman from squad after BCCI directive

    KKR confirms exclusion of Mustafizur Rahman from squad after BCCI directive

    File photo of Mustafizur Rahman. The BCCI has asked Kolkata Knight Riders to release the fast bowler due to recent developments in Bangladesh.
    | Photo Credit: Emmanual Yogini

    Continue Reading

  • #15/18 Maine picks up road win at #6 Denver

    #15/18 Maine picks up road win at #6 Denver

    Continue Reading

  • Eagles Open 2026 With Matchup Against Charlottetown Islanders

    Eagles Open 2026 With Matchup Against Charlottetown Islanders

    Continue Reading

  • Oppo India Introduces ‘Live It Your Way’ Film As Part Of Reno15 Series Promotions – BW Marketing World

    1. Oppo India Introduces ‘Live It Your Way’ Film As Part Of Reno15 Series Promotions  BW Marketing World
    2. OPPO Reno 15 Series 5G Coming to India Sooner Than Expected with Exciting Pre-Launch Offers  Techgenyz
    3. Oppo Reno15 Pro Max and Reno15 Pro…

    Continue Reading

  • Short-Handed Warriors Fall to Defending Champion Thunder – NBA

    Short-Handed Warriors Fall to Defending Champion Thunder – NBA

    1. Short-Handed Warriors Fall to Defending Champion Thunder  NBA
    2. Thunder send depleted Warriors to worst loss of season  Reuters
    3. Will Richard: “ball movement, making the simple play, playing off two (feet)”  letsgowarriors.com
    4. Steve Kerr Gives…

    Continue Reading

  • Electric vehicles charging stations load forecasting based on hybrid XGBoost-BiLSTM model

  • Ahmad, N., Ghadi, Y., Adnan, M. & Ali, M. Load forecasting techniques for power system: research challenges and survey. IEEE Access 10, 71054–71090. https://doi.org/10.1109/ACCESS.2022.3187839 (2022).

    Google Scholar 

  • Khan, S. Short-term electricity load forecasting using a new intelligence-based application. Sustainability https://doi.org/10.3390/su151612311 (2023).

    Google Scholar 

  • Udendhran, R. et al. Transitioning to sustainable E-vehicle systems – Global perspectives on the challenges, policies, and opportunities. J. Hazard. Mater. Adv. 17, 100619. https://doi.org/10.1016/J.HAZADV.2025.100619 (2025).

    Google Scholar 

  • Elahe, M. F., Kabir, M. A., Mahmud, S. M. H. & Azim, R. Factors impacting short-term load forecasting of charging station to electric vehicle. Electronics (Switzerland) https://doi.org/10.3390/electronics12010055 (2023).

    Google Scholar 

  • Ran, J., Gong, Y., Hu, Y. & Cai, J. L. EV load forecasting using a refined CNN-LSTM-AM. Electr. Power Syst. Res. 238(August), 2025. https://doi.org/10.1016/j.epsr.2024.111091 (2024).

    Google Scholar 

  • Van Kriekinge, G., De Cauwer, C., Sapountzoglou, N., Coosemans, T. & Messagie, M. Day-ahead forecast of electric vehicle charging demand with deep neural networks. World Electric Veh. J. https://doi.org/10.3390/wevj12040178 (2021).

    Google Scholar 

  • Cheng, S., Wei, Z., Shang, D., Zhao, Z. & Chen, H. Charging load prediction and distribution network reliability evaluation considering electric vehicles’ spatial-temporal transfer randomness. IEEE Access 8, 124084–124096. https://doi.org/10.1109/ACCESS.2020.3006093 (2020).

    Google Scholar 

  • S. Su, H. Zhao, H. Zhang, X. Lin, F. Yang, and Z. Li, Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning. In: 2017 19th International Conference on Intelligent System Application to Power Systems, ISAP 2017. https://doi.org/10.1109/ISAP.2017.8071382. (2017).

  • Tang, D. & Wang, P. Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles. IEEE Trans. Smart Grid 7(2), 627–636. https://doi.org/10.1109/TSG.2015.2437415 (2016).

    Google Scholar 

  • Zhang, Q., Chen, J., Xiao, G., He, S. & Deng, K. TransformGraph: A novel short-term electricity net load forecasting model. Energy Rep. 9, 2705–2717. https://doi.org/10.1016/j.egyr.2023.01.050 (2023).

    Google Scholar 

  • Gao, S. X., Liu, H. & Ota, J. Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms. Adv. Manuf 12(2), 227–251. https://doi.org/10.1007/S40436-023-00461-1/FIGURES/9 (2024).

    Google Scholar 

  • Amini, M. H., Kargarian, A. & Karabasoglu, O. ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electr. Power Syst. Res. 140, 378–390. https://doi.org/10.1016/J.EPSR.2016.06.003 (2016).

    Google Scholar 

  • Louie, H. M. Time-series modeling of aggregated electric vehicle charging station load. Electr. Power Compon. Syst. 45(14), 1498–1511. https://doi.org/10.1080/15325008.2017.1336583 (2017).

    Google Scholar 

  • A. Gautam, A. K. Verma, and M. Srivastava. A novel algorithm for scheduling of electric vehicle using adaptive load forecasting with vehicle-to-grid integration. In: 2019 8th International Conference on Power Systems: Transition towards Sustainable, Smart and Flexible Grids, ICPS 2019. https://doi.org/10.1109/ICPS48983.2019.9067702. (2019).

  • Almaghrebi, A., Aljuheshi, F., Rafaie, M., James, K. & Alahmad, M. Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods. Energies (Basel) https://doi.org/10.3390/en13164231 (2020).

    Google Scholar 

  • Peng, Y. & Unluer, C. Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms. Resour. Conserv. Recycl. 190, 106812. https://doi.org/10.1016/j.resconrec.2022.106812 (2022).

    Google Scholar 

  • Zhu, J., Yang, Z., Guo, Y., Zhang, J. & Yang, H. Short-term load forecasting for electric vehicle charging stations based on deep learning approaches. Appl. Sci. (Switzerland) https://doi.org/10.3390/app9091723 (2019).

    Google Scholar 

  • Aduama, P., Zhang, Z. & Al-Sumaiti, A. S. Multi-feature data fusion-based load forecasting of electric vehicle charging stations using a deep learning model. Energies (Basel) https://doi.org/10.3390/en16031309 (2023).

    Google Scholar 

  • Van Kriekinge, G., De Cauwer, C., Sapountzoglou, N., Coosemans, T. & Messagie, M. Day-ahead forecast of electric vehicle charging demand with deep neural networks. World Electr. Veh. J. 12(4), 178. https://doi.org/10.3390/WEVJ12040178 (2021).

    Google Scholar 

  • Li, Y. et al. Probabilistic charging power forecast of EVCS: reinforcement learning assisted deep learning approach. IEEE Trans. Intell. Veh. 8(1), 344–357. https://doi.org/10.1109/TIV.2022.3168577 (2023).

    Google Scholar 

  • Zhou, D. et al. Using Bayesian deep learning for electric vehicle charging station load forecasting. Energies 15(17), 6195. https://doi.org/10.3390/EN15176195 (2022).

    Google Scholar 

  • Mohammad, F., Kang, D. K., Ahmed, M. A. & Kim, Y. C. Energy demand load forecasting for electric vehicle charging stations network based on ConvLSTM and BiConvLSTM architectures. IEEE Access 11, 67350–67369. https://doi.org/10.1109/ACCESS.2023.3274657 (2023).

    Google Scholar 

  • Naveed, M. S. et al. Enhanced accuracy in solar irradiance forecasting through machine learning stack-based ensemble approach. Int. J. Green Energy https://doi.org/10.1080/15435075.2025.2450468 (2025).

    Google Scholar 

  • Naveed, M. S. et al. Leveraging advanced AI algorithms with transformer-infused recurrent neural networks to optimize solar irradiance forecasting. Front. Energy Res. https://doi.org/10.3389/fenrg.2024.1485690 (2024).

    Google Scholar 

  • Hanif, J. M. M. F. et al. The Solar AI Nexus: Reinforcement Learning Shaping the Future of Energy Management (Wiley, 2025).

    Google Scholar 

  • D.-E. L. Yuvaraj Natarajan, Sri Preethaa K. R, Gitanjali Wadhwa, Young Choi, Zengshun Chen, D.-E. Lee, and Y. M. Scholar, SciProfilesScilitPreprints.orgGoogle. Enhancing building energy efficiency with IoT-driven hybrid deep learning models for accurate energy consumption prediction. Coimbatore 641407, India. https://www.mdpi.com/2071-1050/16/5/1925.

  • ACN-Dataset. https://ev.caltech.edu/dataset.

  • T. Chen, C. G.-P. of the 22nd acm sigkdd international, and undefined 2016. Xgboost: A scalable tree boosting system. dl.acm.orgT Chen, C GuestrinProceedings of the 22nd acm sigkdd international conference on knowledge, 2016•dl.acm.org, 13–17, 785–794. https://doi.org/10.1145/2939672.2939785. (2016).

  • Z. Huang, W. Xu, and K. Yu. Bidirectional LSTM-CRF Models for Sequence Tagging. http://arxiv.org/abs/1508.01991. (2015).

  • Continue Reading

  • Research on multi-stage on-board detection algorithm of track defects of high-speed railway based on the influence mechanism of track defects

  • Liu, X. Z. et al. Correlation analysis between rail track geometry and car-body vibration based on fractal theory[J]. Fractal Fract. 6 (12), 727 (2022).

    Google Scholar 

  • Xiao, X. et al. A bayesian Kalman filter…

  • Continue Reading

  • Beavers Fall at Pacific – Oregon State University Athletics

    Beavers Fall at Pacific – Oregon State University Athletics

    Stockton, Calif. – The Oregon State men’s basketball team fell to Pacific 84-53 Friday evening in Stockton, Calif.
     
    Johan Munch led the Beavers with 12 points and five rebounds on 5-for-10 shooting. Keziah Ekissi also finished with 12 points…

    Continue Reading