System for building balance-point-based seasonal fuel consumption forecasting with the aid of a digital computer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for forecasting seasonal fuel consumption in buildings, such as the Degree Day approach, are limited by their reliance on outdoor temperature and failure to account for thermal insulation and solar radiation, making them impractical and unintuitive for consumers.
Innovation Solution
The Thermal Performance Forecast approach uses empirically-derived inputs, including desired indoor temperature, building insulation, HVAC system efficiency, and internal heating gains, to forecast heating and cooling fuel consumption, providing a more comprehensive and visualizable model.
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 and uses readily available outdoor temperature data, but the accuracy is reduced because it incorrectly assumes linear relationship with outside temperature and neglects thermal insulation effects
Solution Approach 1:
The patent transforms the forecasting approach by changing key parameters from simple outdoor temperature-based degree days to balance point temperature that incorporates building-specific thermal conductivity (UA Total). This parameter change allows the model to account for thermal insulation effects while maintaining computational simplicity. The balance point temperature is derived by plotting historical fuel consumption against outdoor temperature and identifying the x-intercept, which represents the temperature at which the building's internal gains equal heat loss through the envelope.
Solution Approach 2:
The patent introduces balance point temperature as an intermediary concept that mediates between outdoor temperature data and fuel consumption forecasting. This intermediary incorporates building-specific thermal properties (UA Total) into the forecasting model, allowing the system to account for thermal insulation effects without requiring complex direct measurements of heat loss. The balance point temperature serves as a bridge that translates building envelope characteristics into a usable forecasting parameter.
2Measurement precision
If formal energy audit or empirical testing is conducted to quantify building thermal conductivity, then measurement precision is improved, but the ease of operation deteriorates because it requires professional assessment and is non-trivial for average consumers
Solution Approach 1:
The patent enables building owners to self-determine their building's thermal conductivity (UA Total) using readily available utility bills and weather data. Instead of requiring professional energy auditors, the system allows occupants to plot their own historical fuel consumption data against outdoor temperature data and identify their balance point temperature. This self-service approach empowers average consumers to obtain accurate building-specific parameters without needing specialized knowledge or professional assessment.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex thermal conductivity measurement process. Rather than requiring actual professional energy audits, the system uses a graphical plotting method that replicates the essential function of determining UA Total through readily available data. The balance point temperature plot serves as a simplified copy of the comprehensive energy audit process, providing comparable accuracy with minimal effort and no specialized expertise required.
3Measurement precision
If time series modelling approach is used to forecast fuel consumption, then measurement precision is improved by separating thermal conductivity into internal heating gains and auxiliary heating, but ease of operation worsens because it does not lend itself to intuitive visualizations and comparative analysis
Solution Approach 1:
The patent adds a visual dimension to the fuel consumption forecasting process by using graphical plots of fuel consumption versus outdoor temperature. The balance point temperature is determined visually from the x-intercept of the plotted data, and the slope of the line provides intuitive information about the building's thermal conductivity. This graphical representation transforms abstract time series model outputs into visual information that is easily interpretable and allows for intuitive comparison between different building performance scenarios.
Solution Approach 2:
The patent employs visual differentiation through color coding in the graphical displays to enhance intuitiveness. Different scenarios, building configurations, or performance levels can be represented by different colors, allowing users to quickly compare and contrast various forecasting outcomes. This visual coding system makes it easier for non-experts to understand and interpret the results of complex thermal performance calculations.
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.