System for forecasting fuel consumption for indoor thermal conditioning 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 Heating Degree Day approach, are limited by their assumption of linear heating fuel consumption with outdoor temperature, neglect of thermal insulation effects, and inability to separate weather data from user preferences and building-specific parameters, making them inaccurate and unintuitive for consumers.
Innovation Solution
The Thermal Performance Forecast approach uses empirically-derived inputs to estimate heating and cooling fuel consumption based on desired indoor temperature, building insulation, HVAC system efficiency, and internal heating gains, providing a unified solution for both heating and cooling seasons with intuitive visualizations and side-by-side comparison of scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the Heating 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 with outside temperature
Solution Approach 1:
The patent transforms the forecasting approach by changing the fundamental parameters used: instead of using heating degree days with linear temperature assumptions, it employs a heat balance model that incorporates thermal conductivity (U-value), building envelope characteristics, and non-linear heat transfer equations. This parameter transformation enables accurate forecasting while maintaining computational feasibility through structured calculation procedures.
Solution Approach 2:
The patent introduces an intermediary heat balance calculation model that mediates between the simplified Degree Day approach and complex building physics simulations. This intermediary model uses U-values as key intermediaries to represent building envelope performance, bridging the gap between simplicity and accuracy by incorporating thermal insulation effects without requiring full building energy simulation complexity.
2Ease of operation
If the Heating Degree Day approach is used, then the forecasting method is easier to apply, but it fails to account for thermal insulation effects
Solution Approach 1:
The patent changes the core parameters from weather-based degree days to building-specific thermal parameters (U-values, thermal conductivity). By using U-values that quantify thermal insulation performance, the method reliably accounts for insulation effects while maintaining ease of application through standardized calculation procedures that can be applied without complex building audits.
Solution Approach 2:
The patent performs preliminary determination of U-values and building envelope characteristics before the forecasting calculation. This preliminary action prepares the necessary thermal parameters in advance, allowing the actual fuel consumption forecast to be calculated easily using these pre-determined values, thus maintaining ease of operation while ensuring reliable inclusion of insulation effects.
3Device complexity
If the Heating Degree Day approach is used, then the forecasting process is simpler, but it cannot separate weather data from user preferences and building parameters
Solution Approach 1:
The patent segments the fuel consumption forecast into distinct components: weather-related degree day calculations, building-specific U-value parameters, and user-controlled operational parameters. This segmentation allows each factor to be independently identified and analyzed, preventing loss of information about the relationship between weather data, building characteristics, and user preferences while maintaining a structured and manageable forecasting process.
Solution Approach 2:
The patent uses U-values as intermediary parameters that connect weather data with building-specific and user-specific factors. The heat balance model serves as an intermediary framework that processes weather inputs through building thermal characteristics (U-values) to produce forecasts that reflect the separate influences of weather, building envelope, and operational choices, thereby preserving information about each factor's contribution.
4Measurement precision
If formal energy audit or empirical testing is conducted to determine thermal conductivity, then measurement precision improves, but loss of time and productivity deteriorate
Solution Approach 1:
The patent uses a standardized U-value parameter that copies or represents the essential thermal conductivity information without requiring actual physical measurement or audit. By using established U-value data from building codes, manufacturer specifications, or simplified assessment methods, the system achieves sufficient measurement precision for forecasting purposes while avoiding the time-consuming nature of formal energy audits or empirical testing.
Solution Approach 2:
The patent employs readily available U-value parameters that can be obtained through simple documentation review or basic calculations rather than expensive, time-intensive measurement campaigns. These U-value inputs serve as sufficient proxies for thermal conductivity, providing adequate precision for forecasting without the productivity loss associated with comprehensive building audits or on-site empirical testing.
Data Source
AI summary
A Thermal Performance Forecast approach is described that can be used to forecast heating and cooling fuel consumption based on changes to user preferences and building-specific parameters that include indoor temperature, building insulation, HVAC system efficiency, and internal gains. A simplified version of the Thermal Performance Forecast approach, called the Approximated Thermal Performance Forecast, provides a single equation that accepts two fundamental input parameters and four ratios that express the relationship between the existing and post-change variables for the building properties to estimate future fuel consumption. The Approximated Thermal Performance Forecast approach marginally sacrifices accuracy for a simplified forecast. In addition, the thermal conductivity, effective window area, and thermal mass of a building can be determined using different combinations of utility consumption, outdoor temperature data, indoor temperature data, internal heating gains data, and HVAC system efficiency as inputs.