Environment controller and method for inferring via a neural network one or more commands for controlling an appliance
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Solution Overview
Problem
Current environment control systems fail to generate optimal commands for appliances based on current environmental conditions and set points, as they do not adequately consider multiple, potentially complex criteria such as comfort, stress on components, and energy consumption, leading to either inefficient or overly complex rule-based models.
Innovation Solution
An environment controller equipped with a neural network inference engine that uses a predictive model generated by a training engine to infer commands for controlling appliances, considering environmental characteristic values and set points, thereby optimizing command generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a quick convergence from current temperature to target temperature is implemented when the difference is significant, then comfort of people in the room is improved, but stress imposed on components of the controlled appliance and energy consumption increase
Solution Approach 1:
The patent implements dynamic control strategies that adapt the convergence speed based on real-time conditions. When temperature difference is large, the system prioritizes comfort with faster convergence; when difference is small, it switches to energy-saving mode with slower convergence. This dynamic adjustment resolves the contradiction between comfort and energy consumption.
Solution Approach 2:
The system changes control parameters (convergence speed, heating/cooling intensity) based on the magnitude of temperature difference. By adjusting these parameters dynamically, the system optimizes the balance between comfort (quick convergence when needed) and energy consumption (slower convergence when acceptable).
2Reliability
If a quick convergence from current temperature to target temperature is implemented when the difference is significant, then comfort of people in the room is improved, but stress imposed on components of the controlled appliance increases
Solution Approach 1:
The control system dynamically adjusts the convergence rate based on the temperature difference magnitude. For large differences, it allows faster convergence to ensure comfort; for small differences, it uses slower convergence to reduce stress on components. This dynamic behavior resolves the contradiction between comfort and component stress.
3Adaptability or versatility
If a rule-based model is designed to take into consideration multiple complex and inter-related criteria for generating adequate commands, then the model becomes too complicated to be designed by a human being
Solution Approach 1:
The patent replaces complex mechanical rule-based reasoning with a neural network model that learns optimal control strategies from data. The neural network automatically handles multiple inter-related criteria (comfort, energy consumption, component stress) without requiring explicit programming of complex rules, thus reducing design complexity while maintaining adaptability.
Solution Approach 2:
The neural network model trains itself to understand the relationships between multiple criteria and generate appropriate commands. Instead of requiring human experts to manually design complex rules considering all inter-related factors, the system self-learns from operational data, automatically adapting to optimize comfort, energy consumption, and component stress simultaneously.
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 receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). 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 based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.