Environment controller and method for inferring one or more commands for controlling an appliance taking into account room characteristics
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Solution Overview
Problem
Current environment control systems fail to generate optimal commands for appliances by not adequately considering both environmental characteristic values and room characteristics, leading to inefficient energy use and stress on appliance components.
Innovation Solution
An environment controller equipped with a neural network inference engine that uses a predictive model to determine commands based on environmental characteristic values, set points, and room characteristics, optimizing the control of appliances such as HVAC systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If quick convergence from current temperature to target temperature is prioritized, then comfort of people in the room is improved, but stress on controlled appliance components and energy consumption increase
Solution Approach 1:
The system dynamically adjusts control commands based on real-time environmental characteristics and room characteristics. The neural network model selects different control strategies depending on the temperature difference magnitude, transitioning between aggressive cooling/heating for large differences and conservative control for small differences, thereby optimizing both comfort and energy consumption dynamically
Solution Approach 2:
The system changes control parameters (command strength, convergence speed) based on the magnitude of temperature difference and room characteristics. For large temperature differences, stronger commands are issued to achieve quick convergence; for small differences, weaker commands are used to minimize energy consumption while maintaining comfort
2Ease of operation
If quick convergence from current temperature to target temperature is prioritized, then comfort of people in the room is improved, but stress on controlled appliance components increases
Solution Approach 1:
The control strategy dynamically adapts based on temperature difference magnitude and room characteristics. The neural network model adjusts the aggressiveness of control commands in real-time, using stronger commands only when necessary for large temperature differences and milder commands when the system is near the target, thereby protecting appliance components while maintaining comfort
Solution Approach 2:
The system modifies control parameter magnitudes based on the situation. For large temperature differences, stronger control actions are applied to achieve quick convergence; for small differences, weaker actions are taken to reduce stress on appliance components, thus reliability is improved while comfort is maintained
3Loss of energy
If conservative control is used to minimize energy consumption and stress on components, then energy efficiency is improved, but convergence speed from current temperature to target temperature decreases
Solution Approach 1:
The system dynamically adjusts control aggressiveness based on the magnitude of temperature difference. When the temperature difference is large, the neural network model selects faster convergence strategies despite higher energy consumption. When the temperature difference is small, conservative control is applied to minimize energy usage, thus optimizing the trade-off between speed and energy efficiency dynamically
Solution Approach 2:
The control parameters are adjusted based on the temperature difference magnitude and room characteristics. For large differences, parameters are set to prioritize speed; for small differences, parameters are set to prioritize energy efficiency. This adaptive parameter adjustment resolves the contradiction between convergence speed and energy consumption
4Reliability
If conservative control is used to minimize stress on controlled appliance components, then reliability of controlled appliance is improved, but convergence speed from current temperature to target temperature decreases
Solution Approach 1:
The control system dynamically adjusts its aggressiveness based on real-time conditions. The neural network model selects control strategies that balance reliability and speed according to the temperature difference magnitude and room characteristics, using stronger commands only when necessary and milder commands when the system is near the target, thus optimizing both reliability and convergence speed
Solution Approach 2:
Control parameters are adaptively changed based on the situation. For large temperature differences, parameters allow faster convergence despite higher stress on components. For small differences, parameters are adjusted to minimize stress while maintaining acceptable convergence speed. This adaptive approach resolves the contradiction between reliability and speed
5Device complexity
If control commands are generated without considering room characteristics, then device complexity is reduced, but adequacy of commands for specific room conditions deteriorates
Solution Approach 1:
The control system segments the problem by separately processing environmental characteristics and room characteristics through the neural network model. The room characteristics (geometry, volume, insulation) are pre-stored and segmented from real-time sensor data, allowing the system to handle complexity in a modular way while improving command adequacy for specific room conditions
Solution Approach 2:
The neural network model acts as an intermediary that processes both environmental characteristics and room characteristics to generate optimized control commands. This intermediary layer handles the complexity of integrating multiple factors, allowing the controller to adapt to specific room conditions without requiring complex rule-based logic in the main control system
Data Source
AI summary
Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance. The inference is based on the at least one environmental characteristic value, the at least one set point and the at least one room characteristic. The environment controller transmits the one or more commands to the controlled appliance.