System and method for forecasting fuel consumption for indoor thermal conditioning using thermal performance forecast approach with the aid of a digital computer
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Solution Overview
Problem
Current methods for forecasting seasonal fuel consumption for indoor thermal conditioning, such as the Degree Day approach, are limited by their reliance on outdoor temperature, failure to account for thermal insulation effects, and lack of intuitive visualization, making it difficult for consumers to understand the impact of changes to building thermal properties or HVAC systems.
Innovation Solution
The Thermal Performance Forecast approach uses empirically-derived inputs to forecast heating and cooling fuel consumption based on user preferences, building-specific parameters like thermal conductivity, and internal heating gains, providing a unified solution for both heating and cooling seasons with intuitive visualization and side-by-side comparison capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the Degree Day approach is used to forecast seasonal fuel consumption, then the forecasting process is simplified, but the accuracy deteriorates due to incorrect linear assumption and neglect of thermal insulation effects
Solution Approach 1:
The patent transforms the forecasting approach by changing key parameters: instead of using degree days with linear assumptions, it employs balance point temperature that accounts for thermal insulation (UATotal), internal heating gains (QInternal), and solar savings fraction. This parameter transformation enables accurate modeling of non-linear thermal behavior while maintaining forecastability through empirically-derived parameters.
Solution Approach 2:
The patent replaces the mechanical Degree Day calculation system with a thermal balance system that incorporates building-specific thermal properties. The new system substitutes the simplified degree day metric with a comprehensive thermal model that includes UATotal, QInternal, and balance point temperature, thereby replacing an inadequate mechanical approximation with a more accurate thermal physics-based approach.
2Measurement precision
If formal energy audit or empirical testing is conducted to quantify building thermal conductivity, then measurement precision improves, but loss of time and ease of operation deteriorate
Solution Approach 1:
The patent enables the building itself to provide the thermal conductivity information through its operational fuel consumption data. By analyzing historical fuel usage patterns against outdoor temperature data, the system allows the building to self-reveal its thermal properties (UATotal, QInternal, balance point temperature) without requiring external audits or specialized testing equipment. The building's own operational data becomes the measurement source.
Solution Approach 2:
The patent introduces fuel consumption data as an intermediary that connects observable operational parameters to hidden thermal properties. Instead of directly measuring thermal conductivity through audits, the system uses fuel consumption as a mediator that encapsulates the building's thermal behavior, allowing indirect but accurate determination of UATotal and other thermal parameters through data analysis.
3Measurement precision
If time series modelling approach is used to forecast fuel consumption, then measurement precision improves, but ease of operation and visualization capability deteriorate
Solution Approach 1:
The patent changes the parameter representation from abstract time series coefficients to physically meaningful thermal parameters (balance point temperature, UATotal, QInternal). This transformation allows the forecast model to maintain high accuracy while producing results that can be visualized and understood in terms of familiar thermal concepts, bridging the gap between statistical accuracy and intuitive comprehension.
Solution Approach 2:
The patent applies visual metaphors by representing thermal performance through color-coded or graphically distinct parameters such as balance point temperature and thermal conductivity zones. This visual encoding transforms complex time series forecast data into intuitive graphical representations that convey thermal performance characteristics at a glance, making the forecast both accurate and easily interpretable.
Data Source
AI summary
A system and method for forecasting fuel consumption for indoor thermal conditioning using thermal performance forecast approach with the aid of a digital computer are provided. Average daily outdoor temperatures for a time period are obtained. Historical daily fuel consumption for the time period is obtained. The historical daily fuel consumption is converted into an average daily fuel usage rates for the time period. A continuous frequency distribution of occurrences of the average daily outdoor temperatures is generated. A plot of the daily fuel usage rates versus the average daily outdoor temperatures is created. Fuel consumption for at least a portion of the time period is calculated based on sampling the daily fuel usage rate along a range of average daily outdoor temperatures times the temperatures' respective frequencies of occurrence.