An off-grid, grid-connected storage charging and switching management method
By combining sensor data collection, edge computing, and deep reinforcement learning models with blockchain technology, the lack of flexibility and intelligence in existing off-grid and grid-connected switching strategies has been solved, enabling intelligent energy management and security recording.
CN119966070BActive Publication Date: 2026-06-05HUNAN GNOO NEW ENERGY TECH CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUNAN GNOO NEW ENERGY TECH CO LTD
- Filing Date
- 2025-01-17
- Publication Date
- 2026-06-05
AI Technical Summary
Technical Problem
Existing off-grid and on-grid switching strategies lack flexibility and intelligence, and cannot effectively respond to environmental changes and user behavior patterns, resulting in low energy utilization efficiency and system instability.
Method used
Data is collected by sensors, preprocessed, and transmitted to local edge computing nodes. Energy demand is predicted and adjusted using time series analysis and deep reinforcement learning models. Blockchain technology is used to record switching information to achieve intelligent decision-making and security recording.
Benefits of technology
It enables flexible and efficient energy management, improves energy utilization efficiency, and ensures system stability and data security.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN119966070B_ABST
Abstract
The application discloses an off-grid and grid-connected storage and charging switching management method and relates to the field of intelligent energy management. The method comprises the following steps: collecting data through installed sensors to obtain intelligent energy management comprehensive data, preprocessing the collected intelligent energy management comprehensive data to form an intelligent energy management comprehensive data set, transmitting the formed intelligent energy management comprehensive data set to a local edge computing node through wireless sensor network technology, obtaining a future energy demand change trend through time series analysis, generating a power adjustment strategy according to the future energy demand change trend, and adjusting the power by using a deep reinforcement learning model of an intelligent energy management panel. When a trigger condition is detected, a switching program is immediately started to convert off-grid to grid-connected. The application realizes comprehensive data coverage through data collection by installed sensors.
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