HVAC system control integrated with demand response, on-site energy storage system and on-site energy generation system

• US 9,651,929 B2
• Filed: 09/29/2014
• Issued: 05/16/2017
• Est. Priority Date: 09/29/2014
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Lee, Young M., Liberti, Leo S., Horesh, Raya
• Primary Examiner(s): Barnes-Bullock, Crystal J
• Application Number:
  US14/500,190
• Publication Number:
  US 20160091904A1
• Time in Patent Office: 960 Days
• 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 11/47 Responding to energy costs
• CPC Class Codes: F24F 11/52 Indication arrangements, e.g. displays
• CPC Class Codes: F24F 11/58 using Internet communication
• CPC Class Codes: F24F 11/63 Electronic processing
• CPC Class Codes: F24F 11/74 for controlling air flow rate or air velocity
• CPC Class Codes: F24F 11/80 for controlling the temperature of the supplied air
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: F24F 2120/10 Occupancy
• 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 13/02 electric
• CPC Class Codes: G05B 13/04 involving the use of models or simulators
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05D 23/1904 variable in time
• CPC Class Codes: G05D 23/1917 using digital means
• CPC Class Codes: G05D 23/1923 using thermal energy, the cost of which varies in function of time
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2203/20 : Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/003 : Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: Y02P 80/10 : Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• CPC Class Codes: Y04S 50/16 : Energy services, e.g. dispersed generation or demand or load or energy savings aggregation

Abstract

A thermal behavior model of a building may be constructed based on time series data. Based on the constructed thermal behavior model, forecasted zone temperature and energy usage for a next control time period may be predicted. An objective function may be constructed based on at least a dynamically priced grid energy cost, occupant comfort matrix, and one or more of: energy storage system cost and associated operational cost, and energy generation system cost and associated green house emission cost and associated operational cost. Constraints may be constructed based on at least the forecasted zone temperature values and energy usage values for the next control time period. A control profile of the HVAC system and sourcing decision of energy load of the HVAC system may be determined simultaneously based on the objective function and the plurality of constraints.

Claims

1. A method of controlling an HVAC system, comprising:

receiving a time series data of at least a zone temperature associated with a building zone, energy usage associated with the building zone, building operations associated with the building zone and ambient temperature, at least some of the time series data received from a building automation controller that is connected to at least a sensor or a meter or both in a building;

constructing a thermal behavior model of the building zone based on the time series data;

predicting based on the constructed thermal behavior model at least forecasted zone temperature values and energy usage values for a next control time period;

constructing an objective function based on at least a dynamically priced grid energy cost and associated greenhouse gas emission, occupant comfort matrix, and one or more of;

energy storage system cost and associated operational cost, and

energy generation system cost and associated green house emission cost and associated operational cost;

constructing a plurality of constraints based on at least the forecasted zone temperature values and energy usage values for the next control time period;

determining a control profile of the HVAC system and sourcing decision of energy load of the HVAC system simultaneously based on the objective function and the plurality of constraints; and

transmitting the control profile to the building automation controller for controlling the HVAC system in accordance with the control profile.

2. The method of claim 1, wherein the thermal behavior model comprises a zone temperature model and energy usage.

3. The method of claim 1, wherein the thermal behavior model is modeled by a nonlinear autoregressive neural network.

4. The method of claim 1, wherein the control profile comprises a series of temperature set points of a zone thermostat over the next control time period for the building automation controller to set one or more zone thermostats.

5. The method of claim 1, wherein the control profile comprises a series of supply temperature of an AHU over the next control time period for the building automation controller to set an HVAC device.

6. The method of claim 1, wherein the control profile comprises a series of supply flow rate of an AHU over the next control time period for the building automation controller to set an HVAC device.

7. The method of claim 1, wherein the sourcing decision of energy load comprises sourcing the energy load of the HVAC system from purchased energy, stored energy and generated energy, sourcing the energy load of the HVAC system from purchased energy and stored energy, or sourcing the energy load of the HVAC system from purchased energy and generated energy, wherein the sourcing decision of energy load comprises amount of energy to use from which source and at what time interval over the next control time period.

8. The method of claim 1, wherein the determining a control profile of HVAC system and sourcing decision of energy load of the HVAC system comprises determining the control profile of HVAC system, the sourcing decision of energy load of the HVAC system and an energy storage decision simultaneously based on the objective function and the plurality of constraints.

9. The method of claim 8, wherein the energy storage decision comprises amounts of energy to store at which intervals of time over the next control time period.

10. The method of claim 1, wherein the control profile of HVAC system and the sourcing decision of energy load of the HVAC system are determined for each of multiple building zones simultaneously based on the objective function and the plurality of constraints.

11. A system for controlling an HVAC system, comprising:

a processor;

the processor operable to receive a time series data of at least a zone temperature associated with a building zone, energy usage associated with the building zone, building operations associated with the building zone and ambient temperature,

the processor operable to receive at least some of the time series data from a building automation controller that is connected to at least a sensor or a meter or both in a building,

the processor further operable to construct a thermal behavior model of the building zone based on the time series data,

the processor further operable to predict based on the constructed thermal behavior model at least forecasted zone temperature values and energy usage values for a next control time period,

the processor further operable to construct an objective function based on at least a dynamically priced grid energy cost and associated greenhouse gas emission, occupant comfort matrix, and one or more of (i) energy storage system cost and associated operational cost, and (ii) energy generation system cost and associated green house emission cost and associated operational cost,

the processor further operable to construct a plurality of constraints based on at least the forecasted zone temperature values and energy usage values for the next control time period,

the processor further operable to determine a control profile of the HVAC system and sourcing decision of energy load of the HVAC system simultaneously based on the objective function and the plurality of constraints, and

the processor further operable to transmit the control profile to the building automation controller for controlling the HVAC system in accordance with the control profile.

12. The system of claim 11, wherein the thermal behavior model comprises a zone temperature model and energy usage.

13. The system of claim 11, wherein the thermal behavior model is modeled by a nonlinear autoregressive neural network.

14. The system of claim 11, wherein the control profile comprises:

a series of temperature set points over the next control time period for the building automation controller to set one or more zone thermostats;

a series of supply temperature of an air handling unit over the next control time period for the building automation controller to set an HVAC device;

or

a series of supply flow rate of the air handling unit over the next control time period for the building automation controller to set an HVAC device;

or

combinations thereof.

15. The system of claim 11, wherein the sourcing decision of energy load comprises sourcing the energy load of the HVAC system from purchased energy, stored energy and generated energy, sourcing the energy load of the HVAC system from purchased energy and stored energy, or sourcing the energy load of the HVAC system from purchased energy and generated energy, wherein the sourcing decision of energy load comprises amount of energy to use from which source and at what time interval over the next control time period.

16. The system of claim 11, wherein the processor further determines an energy storage decision simultaneously with the control profile of HVAC system and the sourcing decision of energy load of the HVAC system based on the objective function and the plurality of constraints.

17. The system of claim 16, wherein the energy storage decision comprises amounts of energy to store at which intervals of time over the next control time period.

18. The system of claim 11, wherein the control profile of HVAC system and the sourcing decision of energy load of the HVAC system are determined for each of multiple building zones simultaneously based on the objective function and the plurality of constraints.

19. A computer readable storage medium storing a program of instructions executable by a machine to perform a method of controlling an HVAC system, the method comprising:

receiving a time series data of at least a zone temperature associated with a building zone, energy usage associated with the building zone, building operations associated with the building zone and ambient temperature, at least some of the time series data received from a building automation controller that is connected to at least a sensor or a meter or both in a building;

constructing a thermal behavior model of the building zone based on the time series data;

predicting based on the constructed thermal behavior model at least forecasted zone temperature values and energy usage values for a next control time period;

constructing an objective function based on at least a dynamically priced grid energy cost and associated greenhouse gas emission, occupant comfort matrix, and one or more of;

energy storage system cost and associated operational cost, and

energy generation system cost and associated green house emission cost and associated operational cost;

constructing a plurality of constraints based on at least the forecasted zone temperature values and energy usage values for the next control time period;

determining a control profile of the HVAC system and sourcing decision of energy load of the HVAC system simultaneously based on the objective function and the plurality of constraints; and

transmitting the control profile to the building automation controller for controlling the HVAC system in accordance with the control profile.

20. The computer readable storage medium of claim 19, wherein the determining a control profile of HVAC system and sourcing decision of energy load of the HVAC system comprises determining the control profile of HVAC system, the sourcing decision of energy load of the HVAC system and an energy storage decision simultaneously based on the objective function and the plurality of constraints.