Room Temperature Control Using Neural Saturation Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing temperature control systems, such as PID models, are inefficient in maintaining a comfortable room temperature due to high thermal inertia and non-linear thermodynamic characteristics, leading to large temperature fluctuations and reduced energy efficiency.
Innovation Solution
A neural network-based temperature control system that predicts saturation temperatures using a predicted temperature time series, allowing for robust and fault-tolerant control by determining switch-on and switch-off times without continuous model evaluation, thus reducing computational load and sensitivity to communication errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If PID control is used to maintain room temperature, then the control system is easy to implement, but the temperature fluctuation is large and comfort level deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future saturation temperatures using a neural network model before the temperature actually reaches critical levels. This allows the control system to prepare and execute switching actions at optimal times, preventing large temperature fluctuations rather than reacting to them after they occur.
Solution Approach 2:
The system continuously monitors actual temperature measurements and compares them with predicted saturation temperatures from the neural network model. This feedback mechanism allows the system to adjust switching times dynamically, maintaining temperature within the comfort zone while minimizing fluctuations.
2Manufacturing precision
If neural network-based predictive control is used, then temperature fluctuation is minimized, but computational resources are heavily consumed
Solution Approach 1:
The system extracts only the essential information needed for control decisions from the neural network model - specifically, the predicted saturation temperatures. Rather than continuously evaluating the full neural network model, the system uses a simplified approach that leverages pre-trained model outputs, significantly reducing computational load while maintaining control precision.
Solution Approach 2:
The neural network model is trained in advance offline to learn the thermal characteristics of the room. During actual operation, only inference is performed using stored model parameters, rather than continuous training or full model evaluation. This preliminary preparation transfers computational burden from runtime to training time.
3Measurement precision
If continuous neural network evaluation is performed for control decisions, then accurate saturation temperature prediction is achieved, but the system becomes sensitive to communication errors
Solution Approach 1:
The system extracts only the critical predicted saturation temperature values from continuous neural network evaluations and uses these extracted values for control decisions. This reduces the frequency and volume of data transmission between remote server and local controller, minimizing exposure to communication channel errors while maintaining prediction accuracy.
Solution Approach 2:
The system implements error cushioning by using hysteresis bands and tolerance ranges around predicted saturation temperatures. This creates a buffer zone that accommodates minor communication errors or prediction variations without triggering unnecessary control actions, thereby maintaining system reliability.
4Manufacturing precision
If the temperature control device switches frequently to maintain precise temperature, then comfort level improves, but energy consumption increases
Solution Approach 1:
The system performs preliminary prediction of saturation temperatures to determine optimal switching times in advance. By predicting when temperature will reach saturation points, the system can plan switching actions that maintain temperature stability while avoiding unnecessary frequent switching, thus reducing energy consumption associated with repeated on/off cycles.
Solution Approach 2:
The system uses feedback from actual temperature measurements combined with neural network predictions to optimize switching timing. This feedback mechanism ensures switching occurs only when necessary to maintain temperature within the comfort zone, balancing temperature stability with energy efficiency by eliminating redundant switching events.
Data Source
Figure 1
Figure 2
Figure 3a
AI summary
The present invention relates to a method for controlling the temperature of a room (20) provided with a temperature control device (10), in particular for keeping the temperature of the room (20) within a comfort temperature range defined by a lower and an upper hysteresis temperature (TL, TH), characterized in that -measuring the temperature change of a room (20) and generating first and second temperature time series (01, 02) from the measured temperature data, then using a neural network and using the first and second temperature time series (01, 02), we create predicted first and second temperature time series (P1, P2) indicating the future change of time series (01, 02), - determining predicted saturation temperature values (Tsat(P)) for the elements of the predicted first and second temperature time series (P1, P2), as switch-on and switch-off times (tbe, tki), using the neural network and the first and second temperature time series (01, 02), - selecting from the determined predicted saturation temperature values (Tsat(P)) the closest to the corresponding hysteresis temperature (TH, TL), based on which we determine switch-on and switch-off times (tbe, tki). The invention further relates to a system (100) for carrying out such a method.