Reinforcement Learning Energy Management for Stable Temperature Control
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Solution Overview
Problem
The existing PID control method for energy management systems is inefficient due to slow convergence speed and oscillation, leading to energy wastage and a need for a more stable control method.
Innovation Solution
A method using reinforcement learning with a neural network to control energy management systems by training agents to adjust compressor output and valve opening/closing based on temperature and work-related rewards, optimizing control before and after temperature convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If PID control method is used for energy management, then the control system is simple to implement, but the convergence speed is slow and oscillation occurs
Solution Approach 1:
The patent replaces the traditional PID control mechanism with a reinforcement learning-based neural network control system. The neural network learns optimal control policies through interaction with the environment, substituting the mechanical PID control loop with an intelligent agent that can adaptively optimize control actions, thereby achieving faster convergence and reduced oscillation.
Solution Approach 2:
The patent changes the control parameters dynamically by using a neural network that adjusts control actions based on learned patterns from state observations. Instead of fixed PID parameters, the system adapts control parameters (such as compressor output and valve opening) based on the current system state, enabling faster and more stable convergence to target temperatures.
2Loss of energy
If PID control method is used for energy management, then the implementation is straightforward, but energy waste occurs due to oscillation
Solution Approach 1:
The patent substitutes the simple but energy-inefficient PID control system with a reinforcement learning-based neural network controller. Although the neural network increases computational complexity, it eliminates energy waste by learning optimal control strategies that prevent oscillation and achieve precise temperature control with minimal energy consumption.
Solution Approach 2:
The reinforcement learning agent performs self-learning through interaction with the energy management system environment. The agent autonomously optimizes control policies to minimize energy waste without requiring manual tuning or external intervention, achieving energy efficiency through self-directed learning and adaptation.
3Reliability
If reinforcement learning with neural network is used, then convergence speed and control stability are improved, but the system complexity increases
Solution Approach 1:
The patent segments the control problem into distinct states and actions that the neural network learns to handle. By dividing the control space into manageable state-action pairs, the complex control problem becomes tractable for the reinforcement learning agent, allowing it to achieve stable control without requiring an overly complex system architecture.
Solution Approach 2:
The neural network performs preliminary learning offline through reinforcement learning training before deployment. This preliminary action allows the system to pre-learn optimal control policies, reducing the complexity of real-time control decisions. The agent has already processed complex learning tasks during training, enabling simpler and more stable control execution during actual operation.
Data Source
AI summary
Disclosed is a method for controlling an energy management system that is performed by a computing device including at least one processor. The method may include acquiring a target temperature of one or more target points; and controlling one or more control variables using a reinforcement learning control model trained for a first condition regarding a state before a current temperature of the target points converges to the target temperature.