Heating System Control Based on Dynamic Power Balance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional indoor heating systems face challenges in efficiently controlling temperature due to rapid weather variations and the need for frequent recalibration when radiator capacity changes, leading to energy wastage and inefficiency.
Innovation Solution
A control system that dynamically adjusts the heating power balance by using sensors to detect outdoor and return temperatures, and calculates the required heating power based on indoor temperature, heat transfer coefficients, and heat capacity, allowing for real-time adjustments in forward flow temperature and flow rate to maintain energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional preset temperature curves are used for control, then the control system is simple to implement, but the system requires frequent recalibration when radiator capacity changes and cannot adapt to dynamic weather variations
Solution Approach 1:
The control system automatically detects radiator capacity changes by monitoring temperature deviations and recalibrates the model parameters without user intervention. The system self-adjusts the heat loss coefficients and radiator capacities based on observed thermal responses, eliminating the need for manual recalibration while maintaining model accuracy.
Solution Approach 2:
The system dynamically adjusts control parameters including heat loss coefficients, radiator capacities, and flow temperatures based on detected environmental conditions and system performance. These parameter changes enable the model to adapt to varying radiator configurations and weather patterns without requiring system redesign.
2Manufacturing precision
If trial and error calibration is used during a calibration period, then preset control curves can be established, but the process is time-consuming and difficult to optimize
Solution Approach 1:
The system continuously monitors actual indoor temperatures, outdoor conditions, and heat carrier temperatures to provide feedback on model accuracy. This feedback drives automatic parameter adjustments that rapidly converge to optimal values, replacing lengthy trial-and-error calibration with an efficient iterative optimization process.
Solution Approach 2:
The system performs preliminary parameter estimation using initial measurements and then rapidly refines these parameters through continuous optimization. By establishing initial model parameters before full operation and then automatically adjusting them based on actual performance, the system achieves high accuracy without requiring extended calibration periods.
3Use of energy by moving object
If the system operates without dynamic model-based control, then the control logic is simple, but energy efficiency is reduced due to inability to optimize heating power delivery
Solution Approach 1:
The control system dynamically adjusts flow temperatures and flow rates based on real-time calculations of required heating power. The model continuously adapts to changing outdoor conditions, indoor temperature deviations, and system state, enabling optimal energy delivery that responds to dynamic weather variations and building thermal characteristics.
Solution Approach 2:
The system replaces simple mechanical temperature-based control with a model-based control approach that uses thermal power calculations. By substituting direct temperature control with calculated heating power optimization, the system achieves superior energy efficiency while the computational complexity is managed through efficient algorithms and processing.
4Stability of the object's composition
If rapid weather variations are not accounted for in control, then the control response is simple, but indoor temperature stability deteriorates
Solution Approach 1:
The system performs preliminary calculations of required heating power based on current outdoor conditions and building thermal model before actual heating demand occurs. By anticipating heating requirements based on weather predictions and thermal inertia calculations, the system proactively adjusts flow temperatures to maintain indoor stability during rapid weather changes.
Solution Approach 2:
The system continuously monitors indoor temperature deviations and uses this feedback to adjust heating power delivery. The closed-loop control rapidly responds to temperature fluctuations by modifying flow parameters, maintaining indoor thermal stability even during rapid outdoor weather variations through continuous adaptation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces indoor temperature fluctuations, lowers energy consumption, and adapts to dynamic changes, ensuring a stable indoor climate with lower required temperatures and improved energy efficiency in heating systems.
Implementation Method 1
a sensor for detecting an outdoor temperature
Implementation Method 2
a sensor for detecting a return temperature of a heat carrying medium
Implementation Method 3
heating power transfer through walls of the building
Implementation Method 4
heat carrying medium circulated in a heat distribution system
Data Source
Figure 1~2
Figure 3~4
AI summary
A control system and method for controlling an indoor heating system for an indoor environment in accordance with a desired indoor temperature, comprising a sensor (18) for detecting an outdoor temperature, a sensor (17) for detecting a return temperature Treturn of a heat carrying medium circulated in a heat distribution system with a flow rate, and a controller (13). The controller is adapted to determine a required heating power Preq to be delivered by said heat distribution system to maintain a heating power balance according to Preq = Ploss-Psource, where Ploss is an approximation of heating power losses from said building, and includes a dynamic approximation of heating power transfer through walls of the building, based on at least said desired indoor temperature, said detected outdoor temperature, a heat transfer coefficient of the wall, and a heat capacity of the wall, and Psource is an approximation of heating power sources external to said heating system, and, based on the detected return temperature, control a combination of forward flow temperature and flow rate so as to ensure that the distribution system delivers said required heating power. According to the present invention, a dynamic model of the heating power balance of the building is used to control the heating power delivered by the heating system. This results in improved control compared a control scheme based only on flow temperature control based on outdoor temperature.