AI Energy Flow Control Using Adaptive Simulation Models
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Solution Overview
Problem
Conventional energy flow management systems fail to account for local user behavior and system behavior, leading to inefficiencies in energy management.
Innovation Solution
A computer-implemented method utilizing a simulation model customized by configuration data and trained by an artificial intelligence algorithm to optimize energy flows in controllable devices, incorporating self-learning algorithms like genetic programming to adapt to user and system changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional energy flow management systems are used, then basic energy distribution is achieved, but user behavior and system behavior are not taken into account leading to inefficiency
Solution Approach 1:
The patent implements dynamic adaptation by training AI algorithms with measurement data from actual user behavior and system operation. The simulation model is continuously updated to reflect changing user patterns and system characteristics, allowing the energy management system to adapt its control strategies in real-time rather than relying on static conventional approaches
Solution Approach 2:
The system performs self-learning through AI algorithms that automatically analyze measurement data from the energy system and user behavior patterns. The simulation model self-updates without external intervention, and the trained AI controllers autonomously optimize energy flows based on learned patterns, reducing the need for manual configuration and improving adaptability
2Productivity
If AI algorithms are used to train individual target value controllers, then energy flow optimization is improved, but computing resources and system complexity increase
Solution Approach 1:
The patent divides the energy management system into modular components: a simulation model, individual target value controllers for each controllable device, and AI training algorithms. Each controller is trained independently on specific measurement data, allowing parallel processing and reducing overall computational complexity while maintaining optimization effectiveness
Solution Approach 2:
The system uses a simulation model that replicates the actual energy system behavior. This virtual copy allows AI algorithms to be trained on simulated data alongside real measurement data without risking actual system disruption. The simulation model serves as a safe testing environment that reduces the complexity of direct real-time optimization
Data Source
AI summary
A computer-implemented method for optimizing energy flows of controllable devices within an energy system, the method comprising the steps of: customizing a simulation model of the energy system on the basis of configuration data; training by an artificial intelligence algorithm individual target value controllers associated with controllable devices of the energy system based on measurement data and/or based on generic default data using the customized simulation model of said energy system; calculating by the trained target value controllers target values for the associated controllable devices of the energy system; and controlling energy flow related functions of the controllable devices of said energy system in response to the target values calculated by the trained target value controllers for the respective controllable devices.