Radiant Heating Thermostat Predictive Control for Thermal Inertia
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional heating and cooling systems, particularly radiant heating systems, suffer from overshooting and undershooting due to thermal inertia, leading to inefficient energy use and discomfort, and existing thermostats face challenges in installation and user interface, especially without a 'C' wire connection.
Innovation Solution
A thermostat with a processing system that uses predictive controls, determining a parameterized model based on historical data to select an optimal control strategy for radiant heating systems, allowing for efficient temperature management without the need for a 'C' wire connection, and incorporating advanced user interfaces for easy installation and operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional control systems are used for radiant heating systems, then the system is simple to operate, but the system experiences overshooting and undershooting due to thermal inertia, leading to energy inefficiency and discomfort
Solution Approach 1:
The control system performs preliminary actions by predicting future temperature trends based on historical data and thermal models before overshooting or undershooting occurs. The system proactively adjusts heating operations in advance to prevent temperature deviations, rather than reacting after the problem occurs.
Solution Approach 2:
The system implements continuous feedback by monitoring actual temperature measurements, comparing them with predicted values, and using the differences to refine control decisions. This closed-loop feedback mechanism enables the system to learn from past performance and improve energy efficiency while maintaining comfort.
2Ease of manufacture
If a thermostat without C wire connection is used, then the installation is simpler, but the thermostat lacks sufficient power for advanced processing and user interface functions
Solution Approach 1:
The system changes the power consumption parameters by implementing adaptive power management that adjusts processing intensity, display refresh rates, and wireless communication frequency based on available power from the two-wire configuration. This allows the thermostat to function with limited power while maintaining essential predictive control capabilities.
Solution Approach 2:
The thermostat serves itself by efficiently managing its limited power resources through intelligent duty cycling of power-hungry components and prioritizing essential functions. The system automatically optimizes its operation to survive on the limited power available from the two-wire installation without external power sources.
3Measurement precision
If predictive control algorithms are implemented, then temperature control precision is improved, but the processing requirements and computational load increase
Solution Approach 1:
The control algorithm is segmented into hierarchical levels: simple rule-based control for immediate temperature adjustments and more complex predictive modeling for long-term optimization. This segmentation allows the system to achieve high precision when needed while reducing processing load during stable conditions.
Solution Approach 2:
The system employs periodic action by running full predictive models at scheduled intervals rather than continuously, and using lighter-weight control algorithms between updates. This periodic execution of computational tasks reduces average processing requirements while maintaining temperature control precision through regular model refreshes.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
A method of controlling an environmental control system comprises: providing an environmental control system that includes a processing system in operative communication with a heating system; acquiring historical temperature information regarding heating of an enclosure during at least one historical period in which the enclosure was heated by the heating system under the control of said environmental control system; determining a lag value that represents at least in part an amount of system inertia for the enclosure; determining a plurality of candidate heating control strategies based on said historical temperature information; determining an optimal heating control strategy from said plurality of candidate heating control strategies by computing a plurality of predicted temperature responses corresponding respectively to the plurality of candidate heating control strategies; and controlling the heating system according to the determined optimal heating control strategy.