MUMBAI, India, Aug. 12 -- Intellectual Property India has published a patent application (202631038411 A) filed by Kaushal Kishore; Dr. Taru Tevatia; Maddela Yellamma; Aremanda Lakshmi Rajeswari; Dr. Pinkey Chouhan; Dayananda Sagar Academy Of Technology And Management; Pushpanjali S; S Nishant; Venkata Appaji Sirangi; Dr. S. Kalpana; Dr. U D Prasan; and B Suresh Kumar on March 28, 2026, for An Intelligent Iot-Based Photovoltaic Power Generation And Management Framework Using Machine Learning For Efficient Energy Forecasting And Load Balancing.
Inventors include Kaushal Kishore; Dr. Taru Tevatia; Maddela Yellamma; Aremanda Lakshmi Rajeswari; Dr. Pinkey Chouhan; Pushpanjali S; S Nishant; Venkata Appaji Sirangi; Dr. S. Kalpana; Dr. U D Prasan; and B Suresh Kumar.
The application for the patent was published on August 07, 2026, under issue no. 32/2026.
Abstract: AN INTELLIGENT IOT-BASED PHOTOVOLTAIC POWER GENERATION AND MANAGEMENT FRAMEWORK USING MACHINE LEARNING FOR EFFICIENT ENERGY FORECASTING AND LOAD BALANCING The present invention relates to the development of an intelligent IoT-based photovoltaic power generation and management system is disclosed. In one embodiment, the system includes a photovoltaic power generation system, an energy storage system, and a plurality of controllable loads. Each of the photovoltaic power generation system, the energy storage system, and the controllable loads are implemented using IoT sensor and actuator nodes. Measurement and sensor data from the photovoltaic power generation system, the energy storage system, and the controllable loads, along with other relevant contextual and environmental information, are processed by a machine learning-based forecasting module to provide short-term and day-ahead forecasts for the photovoltaic power generation and controllable loads. The supervisory energy management and load balancing controller processes the forecasted and real-time system states to schedule controllable loads and dispatch energy from the energy storage system and/or import/export energy from the grid based on user- defined priorities and constraints. FIG.1
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