An ai intelligent energy scheduling and management system based on prediction-scheduling-diagnosis

Abstract

This invention discloses an AI-based intelligent energy dispatching and management system based on prediction, scheduling, and diagnosis. The system includes a photovoltaic power generation unit, a hybrid energy storage unit, a thermal energy storage unit, a hydrogen energy storage and conversion unit, an AI intelligent monitoring unit, an AI intelligent management unit, a power grid and load unit, and a cloud platform. The system employs a long short-term memory network to achieve accurate ultra-short-term photovoltaic power prediction, constructs a dynamic optimization scheduling strategy with multiple time scales and energy flows based on deep reinforcement learning, and utilizes a one-dimensional convolutional neural network for real-time fault diagnosis and health management of key equipment. Through the synergy of electricity, heat, and hydrogen energy storage and AI intelligent scheduling, the system significantly improves the self-consumption rate of photovoltaic power generation and system energy efficiency. It possesses intelligent features such as self-sensing of state, self-optimization of scheduling, and self-diagnosis of faults, providing an efficient, reliable, and economical solution for high-proportion renewable energy consumption.

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