Decoupled modeling methods and systems
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Solution Overview
Problem
Existing methods for forecasting the ON/OFF status of thermostatically controlled appliances (TCAs) like HVAC units are inaccurate due to unforeseeable factors such as construction material variations and tree covers, and current models require detailed house specifications, making them impractical for residential energy management systems.
Innovation Solution
A data-driven, decoupled equivalent thermal parameter (ETP) model processor that calculates thermal resistance (R), capacitance (C), and heat flow (Q) parameters at various outdoor temperatures, minimizing errors by adjusting parameters based on daytime and nighttime data, and accounting for solar exposure to improve energy parameter estimation and reduce total energy load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based methods are used to model house thermal dynamics in detail, then model accuracy can be improved, but device complexity and data requirements increase significantly
Solution Approach 1:
The patent transforms the complex physics-based model into a simplified data-driven model by changing the approach from using detailed physical parameters (material composition, thickness, construction details) to using observable operational parameters (power consumption, outdoor temperature, ON/OFF cycles). This parameter transformation maintains modeling capability while eliminating the need for detailed house specifications.
Solution Approach 2:
The patent replaces the physics-based mechanical/thermal model with a data-driven statistical model. Instead of solving differential equations based on thermal physics, the system uses machine learning algorithms trained on power consumption data to predict thermal behavior, substituting complex physical calculations with data-based predictions.
2Measurement precision
If detailed house specifications are required for modeling, then model accuracy improves, but ease of operation deteriorates due to impractical data collection requirements
Solution Approach 1:
The system performs self-characterization by automatically deriving house thermal properties from its own operational data (power consumption patterns, cycling behavior). The model learns and adapts to the specific house characteristics through data collection during normal operation, eliminating the need for manual house specification input or professional assessments.
Solution Approach 2:
The patent changes the input parameters from detailed house specifications (construction materials, insulation values, window types) to easily obtainable operational parameters (power consumption, outdoor temperature, thermostat setpoints). This transformation makes the model practical for real-world deployment where detailed construction data is typically unavailable.
3Device complexity
If single-order ETP models are used, then device complexity is reduced, but measurement precision deteriorates due to inability to capture daytime and nighttime thermal dynamics differences
Solution Approach 1:
The patent divides the single ETP model into separate daytime and nighttime models, each capturing the distinct thermal dynamics of different periods. The daytime model accounts for solar exposure and higher temperatures, while the nighttime model captures cooling-dominated behavior. This segmentation allows each sub-model to be optimized for its specific operating conditions, improving overall accuracy.
Solution Approach 2:
The patent applies different model parameters and characteristics to different time periods (daytime vs. nighttime). Each period receives customized modeling treatment with appropriate thermal parameters, heat flow coefficients, and environmental factors specific to that time of day, rather than using a uniform model for all conditions.
Data Source
AI summary
A decoupled ETP model processor is configured to store power consumption data retrieved from power systems; convert the power consumption data into power activated time cycles and power non-activated time cycles; derive a thermal resistance (R) parameter and a capacitance (C) parameter for a predetermined heat flow (Q) parameter at each of the outdoor temperatures; compare the converted power activated time cycles to the actual power activated time cycles; compare the converted power non-activated time cycles to the actual power non-activated time cycles; calculate a first improved resistance-capacitance-heat flow (RCQ) parameter set and a respective first outdoor temperature for the compared and converted power activated time cycles to the actual power activated time cycles; calculate the Q parameter at each outdoor temperature during the power activated time cycles; and calculate the R parameter and the C parameter at each outdoor temperature during the power non-activated time cycles.