MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202621078343 A) filed by Mr. Sagare Satish Venkatrao; Mr. Hale Laxman Sambhaji; Mr. Balne Gajanan Narsing; Mr. Gaikwad Santosh Balaji; Miss. Nilankar Mamta Devidas; Miss. Mutthe Swati Subhash; and Mrs. Autade Dipali Sahebrao on June 25, 2026, for Ai-Based Smart Energy Monitoring And Load Optimization System.
Inventors include Mr. Sagare Satish Venkatrao; Mr. Hale Laxman Sambhaji; Mr. Balne Gajanan Narsing; Mr. Gaikwad Santosh Balaji; Miss. Nilankar Mamta Devidas; Miss. Mutthe Swati Subhash; and Mrs. Autade Dipali Sahebrao.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: ABSTRACT [505] The global energy management, smart grid, industrial automation, commercial building management, and residential energy efficiency sectors face a critical energy optimization crisis driven by the fundamental inadequacy of conventional energy monitoring methodologies, static load management strategies, and empirical consumption forecasting approaches to meet the extraordinary efficiency, reliability, and sustainability demands of next-generation smart grid infrastructure, renewable energy integration, demand response programs, and carbon-neutral building operations. Energy utility providers, facility management enterprises, industrial manufacturing operations, commercial real estate developers, and smart home technology providers operating across electrical distribution network management, building automation systems, industrial process optimization, renewable energy integration, and electric vehicle charging infrastructure domains generate massive volumes of consumption monitoring data, load profile records, power quality measurements, equipment performance datasets, and environmental condition monitoring streams that conventional empirical load management and static scheduling frameworks are fundamentally incapable of transforming into optimal energy utilization strategies within the efficiency windows demanded by smart grid protocols, sustainability compliance requirements, and cost optimization applications. [510] Existing energy monitoring and load optimization technology platforms exhibit critical deficiencies in their capacity to dynamically optimize power consumption parameters, load scheduling configurations, demand response strategies, equipment operational profiles, and renewable energy integration simultaneously while simultaneously maximizing energy efficiency, minimizing peak demand charges, optimizing power factor correction, ensuring grid stability, and maintaining operational reliability objectives, effectively coordinate multi-building energy optimization across heterogeneous facility types, autonomously adapt load management strategies to real-time grid conditions, integrate machine learning- based consumption prediction into operational decision frameworks, and maintain reliable energy efficiency continuity across weather variability and occupancy pattern fluctuations characteristic of real-world deployment contexts. [515] The integration of Artificial Intelligence capabilities including deep reinforcement learning load optimization, transformer-based consumption prediction, graph neural network grid stability modeling, federated learning multi-site energy coordination, generative AI for optimal scheduling strategy synthesis, and quantum-enhanced power flow optimization presents transformative opportunities for revolutionizing energy management efficiency, smart grid reliability, and sustainable building operations across industrial manufacturing, commercial real estate, residential smart home, and renewable energy integration technological application domains. AI systems capable of learning optimal load management parameters, predicting consumption patterns, coordinating multi-building energy strategies, and exploiting quantum computational advantages for power system optimization from comprehensive operational datasets can autonomously orchestrate energy utilization with efficiency, reliability, and sustainability exceeding conventional empirical management and static scheduling methodologies. [520] The present invention describes a comprehensive AI-Based Smart Energy Monitoring and Load Optimization System that integrates multi-modal sensor data acquisition modules, deep reinforcement learning load optimization engines, quantum-classical hybrid power flow simulation frameworks, federated learning multi-site consumption prediction systems, generative AI optimal scheduling discovery platforms, and adaptive real-time load balancing controllers within a unified autonomous energy management platform. The system continuously analyzes power consumption telemetry, load profile measurements, environmental condition data, occupancy pattern records, equipment performance metrics, and grid status information to orchestrate optimal load scheduling, demand response strategies, renewable energy integration, and equipment operational parameters with energy efficiency, grid stability, and cost optimization exceeding current empirical management and conventional static scheduling methodologies. [525] Validation studies conducted across multiple energy management contexts spanning industrial manufacturing facilities, commercial office buildings, residential smart home deployments, renewable energy microgrids, and electric vehicle charging networks demonstrated that the AI-Based Smart Energy Monitoring and Load Optimization System achieved a 47.8 percent reduction in peak demand charges across heterogeneous facility types, 52.3 percent improvement in overall energy efficiency compared to conventional building management systems, 58.9 percent enhancement in renewable energy utilization efficiency, 43.4 percent reduction in energy waste during non-operational periods, 51.6 percent acceleration in demand response event optimization, and 49.2 percent improvement in predictive accuracy for short-term consumption forecasting compared to conventional empirical load management and static scheduling methodologies. [530] The research findings confirm that the AI-Based Smart Energy Monitoring and Load Optimization System constitutes a foundational technological advancement for smart grid and sustainable building infrastructure, with deployment potential spanning utility companies implementing demand response programs, facility management firms optimizing building operations, industrial manufacturers reducing energy costs, commercial real estate developers achieving sustainability certifications, residential smart home providers delivering intelligent automation, renewable energy operators maximizing grid integration efficiency, and regulatory agencies evaluating energy efficiency compliance submissions requiring intelligent, adaptive, high-performance autonomous energy optimization and AI-enhanced load management capabilities aligned with accelerating global sustainability and carbon reduction demands.
Disclaimer: Curated by HT Syndication.
