Abstract: A system and method for AI-based dynamic pricing, mapping, and emissions control to optimize inner-city mobility. The system acquires real-time nitrogen dioxide data across a geographic area from sources including satellites, feeds this data into a decision support system along with road-specific weight matrices from authorities, and updates a dynamic penalty matrix for roads in the area. Automatic number plate recognition tracks vehicle movement, queries vehicle databases for emissions profiles, and updates the platform. Users input journey parameters and the system, using AI tools including a genetic algorithm economic and environmental dispatch (GA-EED) optimizer, generates an optimized route based on user-selected criteria such as time, emissions, penalty, and cost. The system enables dynamic, environmentally-aware route optimization for inner-city mobility.

Abstract: A system and method for an AI enhanced smart grid framework with optimized incentive capabilities. The present invention optimizes energy efficiency by generating personalized retrofit plans, optimal energy dispatch schedules, and customized financial incentives. Machine learning algorithms analyze homeowner profiles, energy consumption patterns, and market conditions to recommend tailored energy-saving solutions. The system integrates with funding institutions and aggregators to streamline retrofit financing and carbon credit management. By leveraging AI and ML techniques, this innovative approach aims to accelerate the adoption of energy-efficient technologies, reduce homeowner energy costs, and contribute to a more sustainable energy future.