Работы, цитирующие эту работу

Работ: 5

Работа: 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

   СтатьяEnergy Load and Power ForecastingSN Computer Science2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.000661

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

   СтатьяAir Quality Monitoring and ForecastingEngineering Applications of Computational Fluid Mechanics2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.000820

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

   СтатьяHybrid Renewable Energy SystemsInternational Journal of Hydrogen Energy2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.000159

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

   СтатьяEnergy Load and Power ForecastingInternational Journal of Energy Research2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.001789

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

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

   СтатьяEmbedded Systems Design Techniques2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.008467