MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641078217 A) filed by Cmr Engineering College, Kandlakoyav, Medchal Road, Hyderabad, Medchal Malkajgiri, Telangana-, India. on June 24, 2026, for Machine Learning-Driven Energy Consumption Forecasting And Optimization Platform For Smart Buildings.
Inventors include Dr. S. Rama Kishore Reddy, Associate Professor, Electronics And Communication Engineering, Cmr Engineering College; Dr. B. Kishor, Associate Professor, Computer Science And Engineering, Cmr Engineering College, Kandlakoya, Hydeabad-; Mrs. T. Bhavya, Assistant Professor, Computer Science And; Mr. B. Kumaraswamy, Associate Professor, Computer Science; Mrs. Shirisha Thalla, Assistant Professor, Computer Science And; Dr. Shaik Munawar, Associate Professor, Computer Science And; and Ms. B. Revathi, Assistant Professor, Computer Science And Engineering Aiml, Cmr Engineering College, Kandlakoya, Hydeabad-..
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention discloses a Machine Learning-Driven Energy Consumption Forecasting and Optimization Platform for Smart Buildings for intelligent energy management and sustainable building operations. The platform is designed to forecast energy demand and optimize energy usage in residential, commercial, and industrial buildings. Real-time data are collected using IoT-enabled devices including smart meters, temperature sensors, humidity sensors, occupancy sensors, lighting sensors, and HVAC monitoring systems. The collected data undergo preprocessing operations such as cleaning, normalization, feature extraction, and integration to improve data quality and predictive performance. Machine learning algorithms including Linear Regression, Random Forest, XGBoost, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks are employed to analyze historical and real-time energy consumption patterns and forecast future energy demand. Based on predicted consumption levels, an optimization engine dynamically controls HVAC operations, lighting systems, and electrical appliances to minimize energy usage while maintaining occupant comfort. The platform further supports cloud and edge computing architectures for scalable deployment and low-latency processing in smart building environments. Interactive dashboards and analytics tools provide building managers with real-time monitoring, alerts, and decision support. The proposed invention significantly reduces energy consumption, operational costs, and carbon emissions while improving energy efficiency and sustainability. The system is applicable to smart homes, commercial complexes, hospitals, educational institutions, and smart city infrastructures, thereby enabling intelligent and environmentally sustainable energy management solutions.
Disclaimer: Curated by HT Syndication.
