MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641090849 A) filed by Madhankumar C; J. Thavamani - Srm Institute Of Science And Technology; Dr G. Kasiraman - Srm Institute Of Science And Technology; V. Rajasekar - Srm Institute Of Science And Technology; Janani R - Rathinam Technical Campus; P. Nithya - Arunai Engineering College; and R. Anandhan - Dhanalakshmi Srinivasan Engineering College on July 27, 2026, for Ai-Driven Self-Adaptive Hybrid Thermal Energy Management System For Multi-Source Renewable Power Networks.
Inventors include J. Thavamani - Srm Institute Of Science And Technology; Dr G. Kasiraman - Srm Institute Of Science And Technology; V. Rajasekar - Srm Institute Of Science And Technology; Janani R - Rathinam Technical Campus; P. Nithya - Arunai Engineering College; and R. Anandhan - Dhanalakshmi Srinivasan Engineering College Autonomous.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: AI-Driven Self-Adaptive Hybrid Thermal Energy Management System for Multi-Source Renewable Power Networks Abstract The present invention discloses an AI- Driven Self-Adaptive Hybrid Thermal Energy Management System for optimizing thermal regulation, energy utilization, and operational efficiency in multi-source renewable power networks. The proposed system integrates artificial intelligence, machine learning, Internet of Things (IoT) sensors, hybrid thermal storage, edge- cloud computing, and predictive analytics to intelligently manage heat generation, storage, distribution, and recovery across renewable energy sources including solar photovoltaic, concentrated solar power, wind energy, biomass, geothermal systems, and battery energy storage units. The system continuously acquires real-time operational data, including temperature, irradiance, wind speed, ambient conditions, battery state of charge, thermal storage levels, energy demand, and equipment health, through distributed IoT-enabled sensing devices. The collected data are processed using advanced machine learning and deep learning algorithms to predict thermal loads, optimize heat transfer, balance energy distribution, and dynamically regulate cooling and heating mechanisms. Reinforcement learning and predictive optimization enable the system to autonomously adapt to fluctuating renewable energy generation, environmental variations, and load demand while minimizing thermal losses and maximizing overall system efficiency. The proposed architecture incorporates hybrid thermal energy storage technologies, including phase change materials (PCM), molten salt storage, and sensible heat storage, to improve energy retention and ensure continuous power availability during intermittent renewable generation. A digital twin framework continuously models system behavior, enabling predictive maintenance, fault diagnosis, thermal anomaly detection, and intelligent operational planning. Explainable Artificial Intelligence (XAI) provides transparent decision support for energy operators, while blockchain-assisted energy transaction management and secure cloud-based data storage ensure trustworthy, scalable, and resilient operation. The invention further supports smart grid integration, microgrids, electric vehicle charging infrastructure, industrial energy systems, and distributed renewable power plants by enabling autonomous thermal control, intelligent energy scheduling, demand-response optimization, and real-time performance monitoring. The proposed system significantly enhances renewable energy utilization, thermal efficiency, equipment lifespan, grid stability, and operational reliability while reducing energy wastage, carbon emissions, maintenance costs, and overall operating expenses. The disclosed invention provides an intelligent, adaptive, and scalable thermal energy management platform for next-generation sustainable renewable power networks.
Disclaimer: Curated by HT Syndication.
