Hydrogen Energy Storage and Energy Aggregation Systems Utilizing Machine Learning To Interpret Real-Time Telemetry Events

An energy control system employing agentic machine learning techniques to intelligently manage hydrogen production and storage, solar energy production, and interfacing with external systems such as the grid and virtual power plants (VPPs). In accordance with various embodiments of the present invention, a hydrogen storage assembly includes an electrolyzer, a hydrogen storage system, a hydrogen fuel cell, an inverter, an electrochemical energy storage module (e.g., batteries), a power conversion system, and a control system incorporating machine learning techniques, such as a system comprising a real-time telemetry ingestion layer, a machine learning module for anomaly detection and predictive modeling, and a large language model configured to generate context-aware natural language responses based on telemetry, historical data, and user-specific session context, wherein the system provides interactive guidance or decision support through a conversational interface.