System and method for forecasting seasonal fuel consumption for indoor thermal conditioning with the aid of a digital computer

• US 10,359,206 B1
• Filed: 11/03/2016
• Issued: 07/23/2019
• Est. Priority Date: 11/03/2016
• Status: Active Grant

• Current Assignee: Clean Power Research LLC
• Original Assignee: Clean Power Research LLC
• Inventors: Hoff, Thomas E.
• Primary Examiner(s): Vu, Vu A
• Application Number:
  US15/343,079
• Time in Patent Office: 992 Days
• Field of Search: 700276
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 2110/00 Control inputs relating to air properties
• CPC Class Codes: F24F 2130/00 Control inputs relating to environmental factors not covered by group F24F2110/00
• CPC Class Codes: F24F 2130/10 Weather information or forecasts
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

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.

Claims

1. A method for forecasting seasonal fuel consumption for indoor thermal conditioning with the aid of a digital computer, comprising the steps of:

obtaining historical daily fuel consumption for thermal conditioning of a building during a time period;

identifying internal gains within the building over the time period;

adjusting the internal gains based upon season and the amount of fuel required by the HVAC system that provides the thermal conditioning of the building based on the HVAC system efficiency;

accepting ratio terms representing a change in the thermal conductivity of the building, a change in indoor temperature, a change in the internal gains within the building, and a change in the HVAC system efficiency, comprising;

setting a base value for the ratio term representing the change in indoor temperature as the difference of an average indoor temperature minus an average outdoor temperature;

obtaining the HVAC system efficiency;

setting a base value for the ratio term representing the change in the HVAC system efficiency to the HVAC system efficiency;

performing at least one of;

setting a base value for the ratio term representing the change in thermal conductivity ratio to the product of the ratio of thermal conductivity over HVAC system efficiency times the HVAC system efficiency;

setting a base value for the ratio term representing the change in the internal gains ratio to the product of the adjusted internal gains times the HVAC system efficiency; and

estimating the internal gains from a solar resource as a function of the internal gains less the internal gains from electricity and the internal gains, wherein the internal gains comprise internal gains from electricity, internal gains from occupants, and internal gains from the solar resource; and

forecasting seasonal fuel consumption for the building as a function of the historical daily fuel consumption, the adjusted internal gains, and the ratio terms.

2. A method according to claim 1, wherein the forecast seasonal fuel consumption Q^Fuel* is determined in accordance with:

3. A method according to claim 1, further comprising the steps of:

retrieving an average indoor temperature for the building over the time period;

obtaining average daily outdoor temperatures over the time period;

generating a plot of the historical daily fuel consumption averaged on a daily basis versus the average daily outdoor temperatures over the time period;

determining the slope of the plot and converting the slope into average daily fuel usage rate and equating the converted slope of the plot to the ratio of thermal conductivity over HVAC system efficiency; and

equating the x-intercept of the plot to the balance point temperature; and

evaluating the adjusted internal gains as a function of the ratio of thermal conductivity over HVAC system efficiency, difference between average indoor temperature and the balance point temperature, and the duration of the time period.

4. A method according to claim 1, wherein the adjusted internal gains for the time period Q^Adj.Internal is determined in accordance with:

5. A non-transitory computer readable storage medium storing code for executing on a computer system to perform the method according to claim 1.

6. A method for forecasting base seasonal fuel consumption for indoor thermal conditioning with the aid of a digital computer, comprising the steps of:

obtaining historical daily fuel consumption for thermal conditioning of a building during a time period;

identifying internal gains within the building over the time period;

adjusting the internal gains based upon season and the amount of fuel required by the HVAC system that provides the thermal conditioning of the building based on the HVAC system efficiency, comprising at least one of;

determining the adjusted internal gains for the time period Q^Adj.Internal in accordance with;

7. A method according to claim 6, wherein the base seasonal fuel consumption Q^Fuel* is determined in accordance with:

8. A method according to claim 6, further comprising the steps of:

accepting a factor selected from the group consisting essentially of the thermal conductivity of the building, desired indoor temperature of the building, internal gains within the building, and the HVAC system efficiency;

determining a base value for a ratio term for the accepted factor;

accepting a changed value for the ratio term for the accepted factor;

creating the ratio term comprising the changed value over the base value; and

forecasting seasonal fuel consumption for the building as a function of the historical fuel consumption, the adjusted internal gains, and the ratio term.

9. A method according to claim 8, further comprising the step of:

setting a base value for the ratio term representing the change in indoor temperature as the difference of the average indoor temperature minus the average outdoor temperature.

10. A method according to claim 9, further comprising the steps of:

obtaining the HVAC system efficiency; and

setting a base value for the ratio term representing the change in the HVAC system efficiency to the HVAC system efficiency.

11. A method according to claim 10, further comprising the step of:

setting a base value for the ratio term representing the change in thermal conductivity ratio to the product of the ratio of thermal conductivity over HVAC system efficiency times the HVAC system efficiency.

12. A method according to claim 10, further comprising the step of:

setting a base value for the ratio term representing the change in the internal gains ratio to the product of the adjusted internal gains times the HVAC system efficiency.

13. A method according to claim 10, wherein the internal gains comprise internal gains from electricity, internal gains from occupants, and internal gains from the solar resource, further comprising the steps of:

estimating the internal gains from the solar resource as a function of the internal gains less the internal gains from electricity and the internal gains.

14. A non-transitory computer readable storage medium storing code for executing on a computer system to perform the method according to claim 6.

15. A method for function-based forecasting of seasonal fuel consumption for indoor thermal conditioning with the aid of a digital computer, comprising the steps of:

obtaining historical daily fuel consumption for thermal conditioning of a building during a time period;

identifying internal gains within the building over the time period;

adjusting the internal gains based upon season and the amount of fuel required by the HVAC system that provides the thermal conditioning of the building based on the HVAC system efficiency;

accepting ratio terms representing a change in the thermal conductivity of the building, a change in indoor temperature, a change in the internal gains within the building, and a change in the HVAC system efficiency; and

forecasting seasonal fuel consumption for the building as a function of the historical daily fuel consumption, the adjusted internal gains, and the ratio terms, wherein the forecast seasonal fuel consumption Q^Fuel* is determined in accordance with;