Combined Predictive HVAC Models to Improve Operating Point Accuracy
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Solution Overview
Problem
Existing predictive modeling systems for HVAC equipment rely on single models, which can lack accuracy under various operating conditions, limiting their effectiveness in optimizing performance and energy efficiency.
Innovation Solution
A predictive modeling system that combines multiple HVAC component models using different mathematical relationships to generate a combined model prediction, employing techniques like equal weighting, variance weighting, and trimmed mean to optimize operating points and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single predictive model is used to predict HVAC equipment performance, then the model structure is simple and easy to implement, but the prediction accuracy deteriorates under various operating conditions
Solution Approach 1:
The patent combines multiple predictive models (first predictive model and second predictive model) into a combined predictive model that integrates their outputs. This merging approach allows the system to leverage the strengths of different model structures while maintaining overall simplicity, thereby improving prediction accuracy across various operating conditions without significantly increasing system complexity
Solution Approach 2:
The patent creates a composite predictive modeling system that integrates multiple models with different mathematical relationships (e.g., linear regression, neural networks, decision trees). This composite structure combines the advantages of different modeling approaches, enabling accurate predictions across diverse operating conditions while maintaining implementation feasibility through modular architecture
2Adaptability or versatility
If a generalizable predictive model is created to work on a wide variety of HVAC equipment, then the model has broad applicability, but prediction accuracy deteriorates under specific operating conditions
Solution Approach 1:
The patent segments the predictive modeling approach by creating multiple specialized models (first predictive model and second predictive model) that can be selectively applied based on equipment type and operating conditions. This segmentation allows each model to be optimized for specific scenarios while maintaining overall system versatility through conditional model selection and combination
Solution Approach 2:
The patent implements a dynamic model selection and combination mechanism that adapts to different operating conditions. The system dynamically determines which predictive model to use or how to combine models based on real-time equipment state and environmental parameters, thereby maintaining both generalizability across different equipment and high accuracy under specific operating conditions
Data Source
AI summary
A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment, a controller, and a predictive modeling system. The HVAC equipment are operable to affect an environmental condition in the building. The controller is configured to determine an operating point for the HVAC equipment and to operate the HVAC equipment at the operating point. The predictive modeling system includes a plurality of HVAC component models and one or more prediction combiners. The HVAC component models are configured to generate a plurality of component model predictions based on the operating point. The prediction combiners are configured to combine the plurality of component model predictions to form a combined model prediction. The controller is configured to use the combined model prediction to optimize the operating point and to operate the HVAC equipment at the optimized operating point.