Thermodynamic model generation and implementation using observed HVAC and/or enclosure characteristics
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Solution Overview
Problem
Current HVAC control systems lack accuracy in characterizing changes to a thermal environment over time due to actuation of associated HVAC systems, leading to inefficiencies and increased energy costs.
Innovation Solution
A thermodynamic model is generated using multiple basis functions, including a first basis function representing the effect of a previous HVAC state and a second basis function representing the current HVAC state, along with other factors like diurnal sunlight effects, to predict the thermodynamic behavior of an enclosure, allowing for improved prediction of temperature trajectories and system efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple thermodynamic models are used for HVAC control, then device complexity is reduced, but measurement precision and prediction accuracy of thermal environment changes deteriorate
Solution Approach 1:
The thermodynamic model is segmented into multiple basis functions, each representing different physical phenomena (e.g., conduction, convection, radiation, HVAC system response). This segmentation allows the model to capture complex thermal behaviors through simpler, modular components, resolving the contradiction by making the complex model more manageable and implementable.
Solution Approach 2:
The model incorporates dynamic basis functions that adapt to changing thermal conditions and HVAC system states. The basis functions can be selectively activated or adjusted based on operating conditions, allowing the model to maintain high prediction accuracy across varying scenarios without requiring excessive complexity in all conditions simultaneously.
2Measurement precision
If multiple basis functions are used to characterize temperature trajectory, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
The temperature trajectory prediction is segmented into multiple basis functions, each representing a specific thermal phenomenon or time scale. This segmentation enables accurate characterization of complex temperature variations while keeping each individual basis function relatively simple and computationally efficient.
Solution Approach 2:
The model uses parameter changes in the basis functions to capture different thermal behaviors. By adjusting parameters within the basis functions rather than changing the fundamental model structure, the system achieves high prediction accuracy without proportionally increasing structural complexity.
3Use of energy by moving object
If response interval is determined for previous HVAC state, then energy efficiency improves, but loss of time increases
Solution Approach 1:
The model determines the response interval in advance for previous HVAC states, allowing the system to predict future thermal conditions more accurately. This preliminary characterization of system response enables more efficient energy management by anticipating thermal trends without requiring excessively long observation periods.
Solution Approach 2:
The determined response intervals are used as feedback to continuously refine the thermodynamic model predictions. This feedback mechanism allows the system to improve energy efficiency over time by learning from past HVAC responses, reducing the need for prolonged response intervals while maintaining or improving prediction accuracy.
Data Source
AI summary
Techniques for determining and using a thermodynamic model that characterizes a thermodynamic response of an enclosure conditioned by an HVAC system are disclosed. To determine a thermodynamic model, temperature information when the HVAC system operates in a first state may first be received. A response interval may then be determined where the response interval indicates an estimated time between when the HVAC system begins operating in the first state and when the temperature within the enclosure begins to change in a direction associated with the first state. Weighting factors corresponding to basis functions may then be determined, where the weighted basis functions characterize the temperature trajectory of the enclosure in response to the HVAC system operating in the first state. The basis functions may include a first basis function that is evaluated from a time that the HVAC system begins operating in the first state until a time when the response interval ends, and a second basis function that is evaluated beginning at the time when the response interval ends.