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5 ta ish

Ish: Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

  1. Forecasting Smart Grid Stability Using a Hybrid CNN Bi-LSTM Approach

    Divya Singhal, Laxmi Ahuja, Ashish Seth

    MaqolaEnergy Load and Power ForecastingSN Computer Science20240 iqtibos
    ABI

  2. Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

    Loke Kok Foong, Vojtěch Blažek, Lukáš Prokop +3

    MaqolaAir Quality Monitoring and ForecastingEngineering Applications of Computational Fluid Mechanics20240 iqtibos
    ABI

  3. Artificial intelligence applications in hydrogen system: Advancing renewable energy utilization for global hydrogen economy and sustainability goals

    Jiapei Wei, Yingjun Wu, Sanjar Mirzaliev +2

    MaqolaHybrid Renewable Energy SystemsInternational Journal of Hydrogen Energy20250 iqtibos
    ABI

  4. Artificial Intelligence Driven Internet of Things Framework for Wind Energy Monitoring and Performance Enhancement in Smart Cities

    Z. M. S. Elbarbary, Lola Safarova, Farruh Atamurotov +2

    MaqolaEnergy Load and Power ForecastingInternational Journal of Energy Research20250 iqtibos
    ABI

  5. Innovative VLSI System Design and Embedded Architectures Empowered by AI and Machine Learning Advancements

    Ravi Kumar M, Davron Nigmatov, Marwan Aziz Mohammed +3

    MaqolaEmbedded Systems Design Techniques20250 iqtibos
    ABI