US-20260126203-A1 - SYSTEMS AND METHODS FOR DETERMINING OPERATIONAL RELATIONSHIPS IN BUILDING AUTOMATION AND CONTROL NETWORKS

US20260126203A1US 20260126203 A1US20260126203 A1US 20260126203A1US-20260126203-A1

Abstract

Techniques for determining point type, equipment type, equipment instance, and equipment relationship for different points associated with pieces of equipment located at a building is described. The techniques may include obtaining data corresponding to point(s) associated with one or more pieces of equipment located at a building controlled by a building control network, and using the data and statistical model(s) to determine point type(s), equipment type(s), and/or equipment instance(s) for the point(s). The techniques may include determining operational relationships between different pieces of equipment located at the building.

Inventors

Brian Simmons
Jose R. Vazquez-Canteli

Assignees

ONBOARD DATA, INC.

Dates

Publication Date: 20260507
Application Date: 20260102

Claims (20)

1 - 44 . (canceled)
45 . A computer-implemented method, comprising: using at least one hardware processor to perform: obtaining first data from a building control network associated with a first piece of equipment in a building, the first data including time series data corresponding to one or more first measurement points associated with the first piece of equipment; obtaining second data from the building control network associated with a second piece of equipment in the building, the second data including time series data corresponding to one or more second measurement points associated with the second piece of equipment; and determining, using the first data, the second data, and at least one trained statistical model, an operational relationship between the first piece of equipment and the second piece of equipment.
46 . The method of claim 45 , wherein the first piece of equipment is an air handling unit and the second piece of equipment is a variable-air-volume box.
47 . The method of claim 46 , wherein the first data corresponds to a supply fan speed and the second data corresponds to discharge air flow.
48 . The method of claim 46 , wherein the first data corresponds to discharge air temperature of the air handling unit and the second data corresponds to discharge air temperature of the variable-air-volume box.
49 . The method of claim 45 , wherein the method further comprises: selecting, based on a type of the first piece of equipment, a portion of the first data for a first measurement point associated with the first piece of equipment having a first measurement point type; selecting, based on a type of the second piece of equipment, a portion of the second data for a second measurement point associated with the second piece of equipment having a second measurement point type; and determining, using the portion of the first data, the portion of the second data, and the at least one trained statistical model, the operational relationship between the first piece of equipment and the second piece of equipment.
50 . The method of claim 49 , wherein the method further comprises performing a correlation process on the portion of the first data and the portion of the second data and using an output of the correlation process as input to the at least one trained statistical model.
51 . The method of claim 50 , wherein the correlation process is a cross-correlation process, and an output of the cross-correlation process is used as input to the at least one trained statistical model.
52 . The method of claim 51 , wherein the output of the cross-correlation process is a time associated with a cross-correlation value.
53 . The method of claim 51 , wherein the method further comprises: determining statistical values for at least one time window in the portion of the first data and the portion of the second data; and using a combination of one or more statistical values for the portion of the first data and one or more statistical values for the portion of the second data as input to the at least one trained statistical model.
54 . The method of claim 53 , wherein the one or more statistical values for the portion of the first data are selected from the group consisting of: a minimum value, a maximum value, a mean value, a median value, a standard deviation value, 5 th percentile value, 25 th percentile value, 33 rd percentile value, 66 th percentile value, 75 th percentile value, and 95 th percentile value.
55 . The method of claim 53 , wherein the at least one time window is selected based on diurnal occupancy patterns in the building.
56 . The method of claim 53 , further comprising determining a difference between the one or more statistical values for the portion of the first data and the one or more statistical values for the portion of the second data, and using the difference as input to the at least one trained statistical model.
57 . The method of claim 45 , wherein the at least one trained statistical model comprises at least one classifier selected from the group consisting of: a support vector machine classifier, a logistic regression classifier, a gradient boosted classifier, a decision tree classifier, a Bayesian classifier, a network Bayesian classifier, a neural network classifier, and a random forest classifier.
58 . The method of claim 45 , wherein the at least one trained statistical model includes at least one neural network.
59 . A system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to: obtain first data from a building control network associated with a first piece of equipment in a building, the first data including time series data corresponding to one or more first measurement points associated with the first piece of equipment; obtain second data from the building control network associated with a second piece of equipment in the building, the second data including time series data corresponding to one or more second measurement points associated with the second piece of equipment; and determine, using the first data, the second data, and at least one trained statistical model, an operational relationship between the first piece of equipment and the second piece of equipment.
60 . At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to: obtain first data from a building control network associated with a first piece of equipment in a building, the first data including time series data corresponding to one or more first measurement points associated with the first piece of equipment; obtain second data from the building control network associated with a second piece of equipment in the building, the second data including time series data corresponding to one or more second measurement points associated with the second piece of equipment; and determine, using the first data, the second data, and at least one trained statistical model, an operational relationship between the first piece of equipment and the second piece of equipment.
61 - 87 . (canceled)
88 . The method of claim 45 , further comprising: monitoring operation of equipment in the building based, at least in part, on the operational relationship between the first piece of equipment and the second piece of equipment, wherein monitoring operation of equipment in the building comprises: identifying, based at least in part on first data values associated with a first measurement point of the one or more first measurement points, second data values associated with a second measurement point of the one or more second measurement points, and the operational relationship between the first piece of equipment and the second piece of equipment, one or more pieces of equipment in the building is in need of service; and outputting a recommendation to calibrate or replace the first piece of equipment, the second piece of equipment and/or a third piece of equipment having an operational relationship with the first piece of equipment and/or the second piece of equipment in response to determining one or more pieces of equipment in the building is in need of service.
89 . The method of claim 88 , wherein identifying one or more pieces of equipment in the building is in need of service comprises predicting the one or more pieces of equipment is likely to fail or is operating inefficiently.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present application claims the benefit under 35 U.S.C. § 119(e) of U.S. Patent Application Ser. No. 63/058,715, filed Jul. 30, 2020 under Attorney Docket No. 00427.70000US00, and entitled “SYSTEMS AND METHODS FOR DETERMINING OPERATIONAL RELATIONSHIPS IN BUILDING AUTOMATION AND CONTROL NETWORKS,” the entire contents of which is incorporated by reference herein. FEDERALLY SPONSORED RESEARCH This invention was made with Government support under Award No. DE-SC0019958 awarded by the U.S. Department of Energy. The Government has certain rights in the invention. FIELD Aspects of the technology described herein relate to analyzing data obtained from building automation and control systems. BACKGROUND Building automation and control systems include systems that monitor, control and record functions of different building systems (e.g., heating, ventilation, and cooling systems, electricity, lighting). As part of automating and controlling these building systems, other devices including sensors, alarms, and setpoints may be associated with equipment used in operating these building systems. These devices may provide data used in controlling these building systems as well as information relating to performance of certain pieces of equipment (e.g., fans, air handling units, boiler, chiller). SUMMARY Some embodiments are directed to a computer-implemented method, comprising using at least one hardware processor to perform: obtaining first data for a first piece of equipment located at a building controlled by a building control network, the first data including time series data corresponding to one or more first points associated with the first piece of equipment; and determining, using the first data and at least one statistical model trained using training data indicating a plurality of point types and a plurality of equipment types for different pieces of equipment, at least one point type for the one or more first points. In some embodiments, the method further comprises obtaining second data for a second piece of equipment located at the building, the second data including time series data corresponding to one or more second points associated with the second piece of equipment; and determining, using the second data and the at least one statistical model, at least one point type for the one or more second points and an equipment type for the second piece of equipment. In some embodiments, the at least on point type includes one or more point types selected from the group consisting of: a sensor, an actuator, a setpoint, and an alarm. In some embodiments, the method further comprises determining, using the first data and the at least one statistical model, an equipment type for the first piece of equipment. In some embodiments, the equipment type is selected from the group consisting of: an air handling unit, a variable-air-volume box, a boiler, a chiller, a fan, a filter, and a thermostat. In some embodiments, the at least one statistical model includes an encoder, and determining the at least one point type for the one or more first points further comprises determining, using the encoder and the first data, at least one feature of the first data. In some embodiments, determining the at least one point type for the one or more first points further comprises providing the first data as an input to the encoder and obtaining an output indicating the at least one feature. In some embodiments, the at least one feature of the first data includes at least one selected from the group consisting of: a mean value for the first data, a median value for the first data, a standard deviation value for the first data, a kurtosis value for the first data, a skewness value for the first data, a minimum value for the first data, a maximum value for the first data, a median absolute deviation value for the first data, a mean absolute deviation value for the first data, and an interquartile range value for the first data, an autocorrelation value for the first data. In some embodiments, the first data corresponds to one point associated with the first piece of equipment, and the at least one feature of the first data includes a correlation value between the first data and second data corresponding to another point of the first piece of equipment. In some embodiments, the at least one feature of the first data includes a percentage of data values in the first data containing a decimal point. In some embodiments, the at least one feature of the first data includes a ratio of consecutive data values in the first data that are substantially similar to a total number of data values in the first data. In some embodiments, the at least one feature of the first data includes a correlation value between the time-series data of the first data and outdoor temperature for a location of the building. In some embodiments, the at least one feature of the first data includes at least one amplitude of one or