System for plot-based building 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 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 user preferences, 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 capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the Degree Day approach is used to forecast seasonal fuel consumption, then the forecasting process is simple and widely applicable, but the accuracy is reduced due to linear assumptions and neglect of thermal insulation effects
Solution Approach 1:
The patent transforms the forecasting approach by changing key parameters: instead of using simple degree days, it employs balance point temperature (which accounts for thermal insulation) and thermal conductivity as fundamental parameters. This allows the model to capture non-linear thermal behavior while remaining computationally tractable and applicable to widespread building types.
Solution Approach 2:
The patent segments the fuel consumption into distinct components: base load consumption (independent of outdoor temperature) and temperature-dependent consumption. This segmentation allows each component to be modeled with appropriate physical relationships, improving overall forecast accuracy while maintaining simplicity.
2Ease of operation
If the Degree Day approach is used, then the method is intuitive and easy to understand, but it fails to separate weather data from user preferences and building-specific parameters
Solution Approach 1:
The patent explicitly segments the forecasting model into distinct input categories: weather data (outdoor temperature), building-specific parameters (thermal conductivity, balance point temperature), and operational parameters (indoor temperature setpoint, occupancy patterns). This segmentation preserves information integrity while allowing flexible application to different scenarios.
Solution Approach 2:
The patent adds dimensional separation by organizing inputs across multiple dimensions: temporal (weather variations), spatial (building envelope properties), and operational (user preferences). This multi-dimensional structure enables clear distinction between different data types while maintaining overall model coherence.
3Measurement precision
If formal energy audit or empirical testing is conducted to determine thermal conductivity, then accurate building thermal properties are obtained, but the process becomes complex and time-consuming
Solution Approach 1:
The patent enables buildings to self-characterize their thermal properties by using actual fuel consumption data and outdoor temperature measurements. The balance point temperature and thermal conductivity are derived automatically from operational data without requiring external audits or specialized testing equipment, making the process self-service and eliminating complex measurement procedures.
Solution Approach 2:
The patent uses fuel consumption data as an intermediary to indirectly determine thermal conductivity. Instead of directly measuring thermal properties through complex audits, the model uses the intermediary variable of fuel consumption (which is readily available from utility bills) to infer the building's thermal characteristics through the established thermal model.
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.