AI Energy Edge Platform for Distributed Demand Forecasting

The patent introduces an AI-based energy edge platform as an intermediary system that coordinates between distributed energy resources and the centralized grid. The platform uses machine learning models to optimize energy transactions, manage demand-response programs, and balance supply-demand dynamics without requiring complex direct coordination between all distributed participants, thus resolving the contradiction between decentralization benefits and coordination complexity

The system dynamically adjusts operational parameters such as energy pricing, demand-response thresholds, and transaction conditions based on real-time market conditions, weather data, and grid status. These parameter changes enable the decentralized system to maintain efficiency and agility while reducing coordination complexity through automated, data-driven decision-making

The patent segments the data processing architecture into multiple layers: edge devices perform local data filtering and preprocessing, the energy edge platform conducts intermediate analysis using machine learning models, and cloud systems handle comprehensive model training and aggregation. This segmentation reduces the computational burden on individual components while maintaining high forecasting precision through collaborative processing

The system performs preliminary data cleaning, normalization, and feature extraction at the edge devices before data reaches the main processing platform. This preliminary action reduces the complexity of subsequent data processing tasks and enables faster, more precise energy demand forecasting with reduced computational requirements