Building Heat Generator Thermal Power Prediction With Machine Learning
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Solution Overview
Problem
Existing thermal power prediction systems for heat generators in buildings do not account for the actual operating conditions of the generation plant, leading to inefficiencies and energy waste due to mismatched climate correlations and lack of closed-loop regulation.
Innovation Solution
A computer-implemented method using machine learning to predict thermal power by detecting environmental and generator parameters, applying linear and non-linear regression models to calculate coefficients, and optimizing generator settings for energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard climate correlation curves are used for thermal power prediction, then the control system is simple to implement, but the prediction accuracy does not match the actual generator performance requirements
Solution Approach 1:
The system transforms the prediction approach by changing from using fixed standard climate correlation curves to dynamically adjusting prediction parameters based on actual generator operating conditions. The machine learning model learns optimal parameter transformations from historical data, adapting the correlation between external temperature and required thermal power to match specific generator characteristics and performance curves.
Solution Approach 2:
The system implements closed-loop feedback by continuously comparing predicted thermal power with actual generator output and using this information to refine future predictions. The machine learning model is trained on historical operational data that includes actual generator responses, creating a feedback mechanism that improves prediction accuracy over time while maintaining system simplicity.
2Measurement precision
If closed-loop regulation based on real generator performance is implemented, then the thermal power prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The system employs self-service by automatically training the machine learning model using historical operational data from the generator itself. The model learns generator-specific characteristics and performance curves directly from the plant's own historical data, eliminating the need for complex manual calibration or external expertise while achieving high prediction accuracy tailored to the specific generator.
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it predicts thermal power requirements, characterizes generator performance, and adapts to different operating conditions. This multi-functionality reduces overall system complexity by consolidating what would otherwise require separate systems into a single integrated solution.
3Adaptability or versatility
If machine learning models are trained on historical operational data, then the prediction adapts to actual generator performance, but the data processing and model training complexity increases
Solution Approach 1:
The system replaces complex mechanical calibration and adjustment procedures with automated machine learning algorithms. Instead of manually tuning system parameters to match generator performance, the ML model automatically learns the relationships from historical data, substituting automated computational processes for manual mechanical adjustment and reducing operational complexity.
Data Source
Figure 1A
Figure 1B
AI summary
Computer-implemented method (100) for training a machine learning system to predict the thermal power supplied by a heat generator located in a predefined environment, particularly in a building, the method (100) comprising the following steps: detecting a set of temperature values of the air of the predefined environment (101); detecting a set of delivery and return temperature values of a heat transfer fluid (102, 103); calculating hourly average values on the sets of values detected (110); calculating values of hourly average power supplied by the heat generator (111); applying a linear regression model (112) to the calculated average power values; applying a non-linear regression model (113) to the average hourly values of the temperature of the air of the predefined environment, to the average hourly values of the delivery temperature of the heat transfer fluid, and to the calculated average power values.