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Dr. Nitin Dhote is a Professor in the Department of Electrical Engineering at St. Vincent Pallotti College of Engineering and Technology, Nagpur, Maharashtra, India. He is engaged in teaching and academic activities in electrical engineering. His contribution focuses on intelligent power systems, energy management, and the application of multimodal AI in smart grids.
The rising need for electrical energy, incorporation of renewable energy sources, and growth of decentralised power generation have posed considerable difficulties for traditional energy management systems. Intelligent grids offer a sophisticated framework for overseeing, regulating, and enhancing the production, transmission, distribution, and utilisation of electricity. Nonetheless, proficient smart grid administration necessitates the amalgamation of diverse data sourced from smart meters, IoT sensors, meteorological monitoring systems, energy storage apparatus, power producing facilities, and grid communication infrastructures. This section introduces a multimodal AI-driven framework for the intelligent management of energy and the optimisation of smart grids. The suggested framework integrates renewable energy forecasting, power consumption prediction, battery energy administration, load equilibrium, fault identification, and dynamic energy optimisation. CNN, LSTM, Transformer, GNN, and reinforcement learning methodologies are evaluated for deriving significant representations and facilitating astute decision-making. The framework offers a theoretical basis for creating adaptive, dependable, and sustainable smart grid systems.