System and method for forecasting seasonal 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 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 forecast 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 and allowing for side-by-side comparisons of different scenarios, while the Approximated Thermal Performance Forecast simplifies this with a single equation using two input parameters and four ratios to estimate future fuel consumption.
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 assumptions about linearity and thermal insulation effects
Solution Approach 1:
The patent changes the fundamental parameters of the forecasting approach by using a physics-based thermal model with variables for thermal conductivity (U-value), balance point temperature, and solar savings fraction. This replaces the Degree Day approach's simplified linear temperature-based parameters, thereby improving accuracy while maintaining reasonable complexity through the use of standard building performance parameters.
Solution Approach 2:
The patent introduces balance point temperature as an intermediary parameter that mediates between outdoor temperature and heating fuel consumption. This balance point temperature accounts for thermal insulation effects and internal heat gains, serving as a mediator that captures the non-linear relationship between temperature and fuel consumption that the direct Degree Day approach misses.
2Ease of operation
If the Degree Day approach is used, then the method is easier to operate, but it fails to separate weather data from user preferences and building-specific parameters
Solution Approach 1:
The patent segments the fuel consumption forecast into distinct components: weather-dependent terms (involving outdoor temperature and balance point temperature), user preference terms (involving desired indoor temperature), and building-specific parameter terms (involving thermal conductivity and solar savings fraction). This segmentation allows each type of data to be separately identified, analyzed, and adjusted independently.
3Measurement precision
If formal energy audit or empirical testing is conducted to determine thermal conductivity, then measurement precision improves, but loss of time and increase in device complexity occur
Solution Approach 1:
The patent uses a computational model that copies or simulates the thermal performance characteristics of the building using standard parameters (U-value, balance point temperature, solar savings fraction) rather than requiring physical empirical testing. This virtual copying approach provides sufficient accuracy for forecast purposes without the time and complexity costs of formal energy audits or on-site thermal 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.