System for plot-based 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 Degree Day approach, are limited by their assumption of linear heating season 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 non-intuitive and inaccurate for visualizing changes in thermal conditioning components or properties.
Innovation Solution
The Thermal Performance Forecast approach uses empirically-derived inputs to forecast heating and cooling fuel consumption based on user preferences, building insulation, HVAC system efficiency, and internal heating gains, providing a simplified equation for estimating future fuel consumption and determining thermal conductivity, effective window area, and thermal mass without on-site visits or empirical testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the Degree Day approach is used to forecast seasonal fuel consumption, then the forecasting method is simple and widely used, but it incorrectly assumes linear heating season fuel consumption with outside temperature, neglects thermal insulation effects, and fails to separate weather data from building-specific parameters
Solution Approach 1:
The patent segments the fuel consumption forecasting model into distinct components: weather data (degree days), building-specific parameters (thermal conductivity, effective window area, thermal mass), and internal gains. This segmentation allows each component to be calculated and analyzed separately, improving accuracy while maintaining clarity in the forecasting process.
Solution Approach 2:
The patent introduces new building-specific parameters (thermal conductivity UATotal, effective window area, thermal mass) that change the forecasting model from a simple linear degree-day approach to a more comprehensive model that accounts for thermal insulation effects and building characteristics. These parameter changes enable more accurate forecasting without completely abandoning the degree-day framework.
2Measurement precision
If formal energy audit or empirical testing is conducted to determine building thermal conductivity, then accurate thermal conductivity data is obtained, but the process is time-consuming and requires on-site visits
Solution Approach 1:
The patent uses historical fuel consumption data and degree-day data as proxies to calculate building-specific parameters like thermal conductivity. Instead of performing physical empirical testing, the system creates a computational model that copies the thermal behavior of the building using readily available data, thereby avoiding time-consuming on-site testing while still obtaining accurate parameter values.
Solution Approach 2:
The system enables the building itself to 'report' its thermal characteristics through its historical fuel consumption patterns. By analyzing how the building actually consumed fuel in response to historical weather conditions, the system automatically derives building-specific parameters without requiring external auditors or on-site testing equipment.
3Measurement precision
If time series modelling approach is used to forecast fuel consumption, then seasonal fuel consumption can be predicted, but the results are not suitable for comparative and intuitive visualizations of seasonal fuel consumption and effects of proposed changes
Solution Approach 1:
The patent transforms the forecasting results from a single time-series prediction into a multi-dimensional analysis that includes baseline fuel consumption, projected fuel consumption under various scenarios, and the incremental effects of proposed changes. This dimensional expansion allows for intuitive comparison of different scenarios and visualizes the impact of thermal conditioning component changes alongside the time series data.
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.