MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641097293 A) filed by Vels Institute Of Science, Technology & Advanced Studiesvistas on August 12, 2026, for Cognitive Iot Framework For Smart Grid Load Forecasting And Energy Optimization.
Inventors include Dr. A. Akila; Dr. R. Parameswari; Dr. R. Padma; Dr S. Mangayarkarasi; Dr V. Vishwa Priya; Dr T. R Nisha Dayana; Ms. Jeyashree R; Dr. A. Angel Cerli; and Dr S Prathiba.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: It discloses a Cognitive Internet of Things (IoT) Framework designed for advanced smart grid load forecasting and energy optimization through the integration of artificial intelligence, real-time data analytics, and adaptive decision-making mechanisms. The proposed system leverages distributed IoT sensors and smart meters to continuously collect high-resolution energy consumption, environmental, and grid status data. These data streams are processed using cognitive computing models, including machine learning and deep learning algorithms, to accurately predict short-term and long-term load demand patterns. The framework incorporates a dynamic feedback loop that enables self-learning and context-aware adaptation to changing grid conditions, consumer behavior, and renewable energy variability. Additionally, the system employs optimization techniques to balance energy supply and demand, reduce peak load stress, minimize transmission losses, and improve overall grid efficiency. It supports integration with renewable energy sources and energy storage systems, ensuring sustainable and resilient grid operations. The framework also features anomaly detection for fault identification and demand response strategies to enable intelligent load shifting and user participation. By combining predictive intelligence with automated control, the proposed Cognitive IoT Framework enhances energy management, reduces operational costs, and contributes to the development of next-generation smart grids with improved reliability, scalability, and environmental sustainability. FIG.1
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