System and method for providing constraint-based heating, ventilation and air-conditioning (HVAC) system optimization with the aid of a digital computer

• US 10,203,674 B1
• Filed: 05/10/2016
• Issued: 02/12/2019
• Est. Priority Date: 02/25/2015
• Status: Active Grant

• Current Assignee: Clean Power Research LLC
• Original Assignee: Clean Power Research LLC
• Inventors: Hoff, Thomas E.
• Primary Examiner(s): Phan, Thai Q
• Application Number:
  US15/151,410
• Time in Patent Office: 1,008 Days
• Field of Search: 703 2, 703 19, 700276, 700291, 705 722, 704 731
• 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 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 11/63 Electronic processing
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: F24F 2110/12 of the outside air
• CPC Class Codes: F24F 2130/20 Sunlight
• CPC Class Codes: F24F 2140/50 Load
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

HVAC load can be shifted to change indoor temperature. A time series change in HVAC load data is used as input modified scenario values that represent an HVAC load shape. The HVAC load shape is selected to meet desired energy savings goals, such as reducing or flattening peak energy consumption load to reduce demand charges, moving HVAC consumption to take advantage of lower utility rates, or moving HVAC consumption to match PV production. Time series change in indoor temperature data can be calculated using only inputs of time series change in the time series HVAC load data combined with thermal mass, thermal conductivity, and HVAC efficiency. The approach is applicable for both winter and summer and can be applied when the building has an on-site PV system.

Claims

1. A system for providing constraint-based HVAC system optimization with the aid of a digital computer, comprising the steps of:

a storage comprising time series net load for a time period during which HVAC load for a building will be shifted, the building comprising a smart thermostat in control of the building'"'"'s HVAC system that is associated with the HVAC load, the time period comprising regular intervals; and

a server coupled to the storage and comprising a processor configured to execute a web service, the web service comprising;

a total load module adapted to set time series total load to equal the time series net load;

an HVAC load module adapted to estimate time series non-HVAC load during the time period, and to find existing time series HVAC load by subtracting the time series non-HVAC load from the time series total load;

an HVAC load shifting module adapted to select an HVAC load shifting strategy bounded at the beginning and ending of the time period by conditions on changes in indoor temperature, and to construct modified time series net load to match the selected HVAC load shifting strategy subject to operational constraints on the HVAC load remaining a positive value and exceeding the HVAC equipment rating;

a modified HVAC load module adapted to find modified time series HVAC load by subtracting the time series non-HVAC load from the modified time series net load, and to find time series change in HVAC load by subtracting the modified time series HVAC load from the existing time series HVAC load; and

an indoor temperature module adapted to iteratively construct time series change in indoor temperature for the time period as a function of the time series change in HVAC load and the thermal mass, thermal conductivity, HVAC efficiency for the building, and to evaluate whether the modified time series HVAC load meets the operational constraints and that the conditions on changes in indoor temperature are satisfied; and

an optimization module adapted to optimize operation of the HVAC system via the smart thermostat, comprising providing the time series change in HVAC load and the time series change in indoor temperature to the smart thermostat via the Internet, wherein the smart thermostat controls the HVAC system based on the provided time series change in HVAC load and the time series change in indoor temperature.

2. A system according to claim 1, further comprising:

the storage further comprising time series PV production for the time period;

the HVAC load module further adapted to find the existing time series HVAC load by adding the time series PV production to the time series total load; and

the modified HVAC load module further adapted to find the modified time series HVAC load by adding the time series PV production to the modified time series net load.

3. A system according to claim 2, wherein at least one of the time series net load comprises one of measured, simulated, and forecasted data, and the PV production comprises one of measured, simulated, and forecasted data.

4. A system according to claim 1, further comprising one of:

a zero boundary module adapted to constrain the changes in indoor temperature to equal zero at the beginning and ending of the time period; and

a range module adapted to constrain the changes in indoor temperature to not change beyond a specified range at the beginning and ending of the time period.

5. A system according to claim 4, wherein the modified time series HVAC load is constrained in accordance with:

6. A system according to claim 1, further comprising:

the indoor temperature module further adapted to iteratively construct the time series change in indoor temperature starting at the beginning of the time period, where each change in indoor temperature calculated applies to the next time interval in the time period following the change in HVAC load.

7. A system according to claim 6, wherein each change in indoor temperature Δ

T<sub>t+Δ

t</sub>^Indoor at time t over the time interval Δ

t is determined in accordance with;

8. A system according to claim 1, further comprising:

the indoor temperature module further adapted to iteratively construct the time series change in indoor temperature starting at the ending of the time period, where each change in indoor temperature calculated applies to the prior time interval in the time period following the change in HVAC load.

9. A system according to claim 8, wherein each change in indoor temperature Δ

T_t^Indoor at time t over the time interval Δ

t is determined in accordance with;

10. A system according to claim 1, further comprising at least one of:

the HVAC load shifting module further adapted to select reducing or flattening peak energy consumption load to reduce demand charges as the HVAC load shifting strategy;

the HVAC load shifting module further adapted to select moving HVAC consumption to take advantage of lower utility rates as the HVAC load shifting strategy; and

the HVAC load shifting module further adapted to select moving HVAC consumption to match the PV production as the HVAC load shifting strategy.

11. A method for providing constraint-based HVAC system optimization with the aid of a digital computer, comprising the steps of:

obtaining by a server comprising a processor configured to execute a web service, time series net load for a time period during which HVAC load for a building will be shifted, the building comprising a smart thermostat in control of the building'"'"'s HVAC system that is associated with the HVAC load, the time period comprising regular intervals;

setting by the server time series total load to equal the time series net load;

estimating by the server time series non-HVAC load during the time period;

finding by the server existing time series HVAC load by subtracting the time series non-HVAC load from the time series total load;

selecting by the server an HVAC load shifting strategy bounded at the beginning and ending of the time period by conditions on changes in indoor temperature;

constructing by the server modified time series net load to match the selected HVAC load shifting strategy subject to operational constraints on the HVAC load remaining a positive value and exceeding the HVAC equipment rating;

finding by the server modified time series HVAC load by subtracting the time series non-HVAC load from the modified time series net load;

finding by the server time series change in HVAC load by subtracting the modified time series HVAC load from the existing time series HVAC load;

iteratively by the server constructing time series change in indoor temperature for the time period as a function of the time series change in HVAC load and the thermal mass, thermal conductivity, and HVAC efficiency for the building;

evaluating by the server whether the modified time series HVAC load meets the operational constraints and that the conditions on changes in indoor temperature are satisfied; and

optimizing by the server operation of the HVAC system via the smart thermostat, comprising providing the time series change in HVAC load and the time series change in indoor temperature to the smart thermostat via the Internet, wherein the smart thermostat controls the HVAC system based on the provided time series change in HVAC load and the time series change in indoor temperature.

12. A method according to claim 11, further comprising the steps of:

obtaining time series PV production for the time period;

finding the existing time series HVAC load by adding the time series PV production to the time series total load; and

finding the modified time series HVAC load by adding the time series PV production to the modified time series net load.

13. A method according to claim 12, further comprising at least one of the steps of:

obtaining the time series net load as one of measured, simulated, and forecasted data; and

obtaining the PV production as one of measured, simulated, and forecasted data.

14. A method according to claim 11, further comprising one of the steps of:

constraining the changes in indoor temperature to equal zero at the beginning and ending of the time period; and

constraining the changes in indoor temperature to not change beyond a specified range at the beginning and ending of the time period.

15. A method according to claim 14, wherein the modified time series HVAC load is constrained in accordance with:

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

iteratively constructing the time series change in indoor temperature starting at the beginning of the time period, where each change in indoor temperature calculated applies to the next time interval in the time period following the change in HVAC load.

17. A method according to claim 16, wherein each change in indoor temperature Δ

T<sub>t+Δ

t</sub>^Indoor at time t over the time interval Δ

t is determined in accordance with;

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

iteratively constructing the time series change in indoor temperature starting at the ending of the time period, where each change in indoor temperature calculated applies to the prior time interval in the time period following the change in HVAC load.

19. A method according to claim 18, wherein each change in indoor temperature Δ

T_t^Indoor at time t over the time interval Δ

t is determined in accordance with;

20. A method according to claim 11, further comprising at least one of the steps of:

selecting reducing or flattening peak energy consumption load to reduce demand charges as the HVAC load shifting strategy;

selecting moving HVAC consumption to take advantage of lower utility rates as the HVAC load shifting strategy; and

selecting moving HVAC consumption to match the PV production as the HVAC load shifting strategy.