Adaptive Stochastic Controller for Energy Efficiency and Smart Buildings

• US 20140249876A1
• Filed: 03/10/2014
• Published: 09/04/2014
• Est. Priority Date: 09/20/2011
• Status: Abandoned Application

• Current Assignee: Trustees Of Columbia University In The City Of New York (Columbia University), Consolidated Edison Incorporated, Rudin Management Co. Inc.
• Original Assignee: Trustees Of Columbia University In The City Of New York (Columbia University), Consolidated Edison Incorporated, Rudin Management Co. Inc.
• Inventors: Wu, Leon L., Boulanger, Albert, Anderson, Roger N., Boniberger, Eugene M., Gilbert, John J., Kressner, Arthur A.
• Application Number:
  US14/203,151
• Publication Number:
  US 20140249876A1
• Time in Patent Office: Days
• US Class Current: 705/7.12
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations

Abstract

Techniques for managing one or more buildings, including collecting historical building data, real-time building data, historical exogenous data, and real-time exogenous data and receiving the collected data at an adaptive stochastic controller. The adaptive stochastic controller can generate at least one predicted condition with a predictive model. The adaptive stochastic controller can generate one or more executable recommendations based on at least the predicted conditions and one or more performance measurements corresponding to the executable recommendations.

Claims

1. A method for managing one or more buildings, comprising:

collecting historical building data, real-time building data, historical exogenous data, and real-time exogenous data;

receiving the collected data at an adaptive stochastic controller; and

with the adaptive stochastic controller;

generating at least one of a predicted condition with a predictive model; and

generating one or more executable recommendations based on the predicted condition and one or more performance measurements corresponding to the executable recommendations.

2. The method of claim 1, wherein the predicted condition includes at least one of the group of predicted space temperature, supply air temperature, chilled water temperature, electric load, steam consumption or fuel consumption

3. The method of claim 1, wherein collecting further comprises receiving from a building management system the historical building data, real-time building data, historical exogenous data, and real-time exogenous data, and wherein the historical building data and the real-time building data includes electric data, fuel and steam data, space temperature information, air flow rate data, chilled water temperature data, supply air temperature information, return air temperature information, lighting sensor data, elevator data, and carbon dioxide data.

4. The method of claim 1, wherein collecting further comprises querying one or more databases including the historical building data, real-time building data, historical exogenous data, and real-time exogenous data.

5. The method of claim 1, wherein collecting further comprises receiving over a network at least one of the historical exogenous data and the real-time exogenous data, and wherein the historical exogenous data and the real-time exogenous data include at least one of historical weather data, forecast weather data, and power grid data.

6. The method of claim 1, further comprising displaying on a user interface trends in one or more building conditions, the predicted conditions, and the one or more executable recommendations.

11. The method of claim 1, wherein the one or more buildings includes a plurality of buildings, and further comprising displaying an efficient frontier (Pareto) curve for efficiency and performance for the plurality of buildings.

12. A system for managing one or more buildings, comprising:

a data collector to collect historical building data, real-time building data, historical exogenous data, and real-time exogenous data; and

an adaptive stochastic controller operatively coupled to the data collector and adapted to receive collected data therefrom, the adaptive stochastic controller comprising;

a predictive model configured to generate at least one predicted condition; and

a decision algorithm configured to generate one or more executable recommendations based on at least the predicted condition and one or more performance measurements corresponding to the executable recommendations.

7. The method of claim 17, further comprising identifying trends in the one or more building conditions and generating a predicted condition for each building condition, and displaying the identified trends and the predicted conditions, whereby an operator is alerted when an anomaly between the predicted conditions and the building conditions arises.

8. The method of claim 7, wherein the one or more building conditions include space temperature at each measurement location of each floor in the one or more buildings.

9. The method of claim 8, wherein generating one or more executable recommendations further includes generating at least one of a recommended start-up time and ramp-down time for a HVAC system based on at least the trends in the one or more building conditions.

10. The method of claim 8, wherein generating one or more executable recommendations further includes generating at least one of a recommended start-up time and ramp-down time for a HVAC system based on at least the trends in the one or more building conditions, the predicted conditions, and the performance measurements.

13. The system of claim 12, wherein the predicted condition includes at least one of the group of space temperature, supply air temperature, chilled water temperature, electric load, steam consumption or fuel consumption

14. The system of claim 12, wherein the data collector is operatively coupled to a building management system, and wherein the historical building data and the real-time building data includes data from at least one of electric meters, fuel and steam sub-meters, chilled water temperature sensors, space temperature and humidity sensors, supply air temperature and humidity sensors, air flow rate sensors, return air temperature and humidity sensors, or carbon dioxide sensors.

15. The system of claim 12, wherein the data collector is operatively coupled to one or more databases including the historical building data, real-time building data, historical exogenous data, and real-time exogenous data.

16. The system of claim 12, wherein the data collector is operatively coupled to a network and configured to receive historical exogenous data and the real-time exogenous data via the network, and wherein the historical exogenous data and the real-time exogenous data include historical weather data, forecast weather data, and power grid data.

17. The system of claim 12, further comprising a user interface configured to display trends in one or more building conditions, the at least one predicted condition, and the one or more executable recommendations.

18. The system of claim 17, wherein the predictive model is further configured to identify trends in the one or more building conditions and generate a predicted condition for each building condition, and wherein the user interface is further configured to display the identified trends and the predicted conditions, whereby an operator is alerted when an anomaly between the predicted conditions and the building conditions arises.

19. The system of claim 18, wherein the one or more building conditions include space temperature at one or more measurement locations of at least one floor in the one or more buildings

20. The system of claim 19, wherein the adaptive stochastic controller is further configured to generate at least one of a recommended start-up time and ramp-down time and rate for a HVAC system based on at least the trends in the one or more building conditions.

21. The system of claim 19, wherein the adaptive stochastic controller is further configured to generate at least one of a recommended start-up time and ramp-down time rate for a HVAC system based on at least the trends in the one or more building conditions, the predicted conditions, and the performance measurements.

22. The system of claim 17, wherein the one or more buildings includes a plurality of buildings, and wherein the user interface is further configured to display an efficient frontier (Pareto) curve for efficiency and performance for the plurality of buildings.