Abstract: A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, generate a time correlated data stream for a data point, and generate a time correlated reliability data stream for the data point. The building device data includes a plurality of data samples of the data point. The time correlated data stream includes values of the plurality of data samples of the data point. The time correlated reliability data stream includes a plurality of reliability values time correlated to corresponding values of the plurality of data samples of the data point and indicating reliability of the values of the plurality of data samples of the data point.

Abstract: A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, the building device data comprising a plurality of data samples of a data point and generate a time correlated data stream for the data point, the time correlated data stream comprising values of the plurality of data samples of the data point. The instructions cause the one or more processors to generate a time correlated reliability data stream for the data point, the time correlated reliability data stream comprising a plurality of reliability values indicating reliability of the values of the plurality of data samples of the data point.

Abstract: A building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.

Abstract: A building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.

Abstract: A building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.

Abstract: A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, the building device data comprising a plurality of data samples of a data point and generate a time correlated data stream for the data point, the time correlated data stream comprising values of the plurality of data samples of the data point. The instructions cause the one or more processors to generate a time correlated reliability data stream for the data point, the time correlated reliability data stream comprising a plurality of reliability values indicating reliability of the values of the plurality of data samples of the data point.

Abstract: A building system of a building including one or more memory devices configured to store one or more instructions that, when executed on one or more processors, cause the one or more processors to exercise a building entity causing building entity data to be generated associated with the building entity, the building entity data indicating a result of exercising the building entity and collect the building entity data. The instructions cause the one or more processors to identify, based on a relational model, one or more relationships between one or more building entities and the building entity, wherein the one or more relationships indicate that exercising the building entity affects operation of the one or more building entities and identify that the building is experiencing a performance issue by analyzing the building entity data and the one or more relationships.

Abstract: A building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.

Abstract: A building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.

Abstract: A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model, using the residual error to calculate an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.

Abstract: A system for detecting faults in building equipment includes an integration fault detector, a kernel density fault detector, and a fault detector selector. The integration fault detector is configured to detect faults in the building equipment by analyzing time series data using an integration fault detection technique. The kernel density fault detector is configured to detect faults in the building equipment by analyzing the time series data using a kernel density estimation fault detection technique. The fault detector selector is configured to select the integration fault detector or the kernel density fault detector for use in detecting faults in the building equipment based on an attribute of the time series data.

Abstract: A building management system (BMS) includes a controller that monitors performance values for a controlled process during a first time period relative to initial outlier detection limits and generates new outlier detection limits for the controlled process in response to a detected change in the controlled process during the first time period. The controller monitors the performance values relative to the new outlier detection limits during a second time period to detect outliers during the second time period. The controller calculates a confidence difference for an estimated confidence parameter based on a number of outliers detected using the new outlier detection limits during the second time period. The controller adjusts the new outlier detection limits in response to the confidence difference dropping below a threshold value.

Abstract: A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model, using the residual error to calculate an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.

Abstract: An operating data aggregator module collects a first set of operating data and a second set of operating data for building equipment. A model generator module generates a first set of model coefficients and a second set of model coefficients for a predictive model for the building equipment using the first set of operating data and the second set of operating data, respectively. A test statistic module generates a test statistic based on a difference between the first set of model coefficients and the second set of model coefficients. A critical value module calculates critical value for the test statistic. A hypothesis testing module compares the test statistic with the critical value using a statistical hypothesis test to determine whether the predictive model has changed. In response to a determination that the predictive model has changed, a fault indication may be generated and/or the predictive model may be adaptively updated.

Abstract: A system for detecting faults in building equipment includes an integration fault detector, a kernel density fault detector, and a fault detector selector. The integration fault detector is configured to detect faults in the building equipment by analyzing time series data using an integration fault detection technique. The kernel density fault detector is configured to detect faults in the building equipment by analyzing the time series data using a kernel density estimation fault detection technique. The fault detector selector is configured to select the integration fault detector or the kernel density fault detector for use in detecting faults in the building equipment based on an attribute of the time series data.

Abstract: A building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.

Abstract: A fault detection and diagnostics (FDD) system is provided for a refrigeration circuit having an evaporator and a compressor configured to circulate a refrigerant through the evaporator. The FDD system includes a communications interface configured to receive a measurement of a thermodynamic property affected by the refrigeration circuit and a processing circuit having a processor and memory. The processing circuit is configured to use the measured thermodynamic property to determine an expected suction entropy of the refrigerant at a suction of the compressor, use the expected suction entropy to determine an expected thermodynamic discharge property of the refrigerant at a discharge of the compressor, determine an actual thermodynamic discharge property of the refrigerant at the discharge of the compressor, and detect a fault in the refrigeration circuit by comparing the expected thermodynamic discharge property with the actual thermodynamic discharge property.

Abstract: A controller for a building management system includes a first data interface configured to receive data from the building management system and a processing circuit including a processor and a memory device storing a fault detection rule having an initial threshold value. The processing circuit is configured to detect a first fault in the building management system using the stored fault detection rule having the initial threshold value and to use the data from the building management system to determine whether an adjustment to the stored fault detection rule is needed. In response to a determination that an adjustment to the stored fault detection rule is needed, the processing circuit is configured to calculate a new threshold value for the stored fault detection rule and update the stored fault detection rule by replacing the initial threshold value with the new threshold value.

Abstract: A building management system (BMS) includes a controller that monitors performance values for a controlled process during a first time period relative to initial outlier detection limits and generates new outlier detection limits for the controlled process in response to a detected change in the controlled process during the first time period. The controller monitors the performance values relative to the new outlier detection limits during a second time period to detect outliers during the second time period. The controller calculates a confidence difference for an estimated confidence parameter based on a number of outliers detected using the new outlier detection limits during the second time period. The controller adjusts the new outlier detection limits in response to the confidence difference dropping below a threshold value.

Abstract: An operating data aggregator module collects a first set of operating data and a second set of operating data for building equipment. A model generator module generates a first set of model coefficients and a second set of model coefficients for a predictive model for the building equipment using the first set of operating data and the second set of operating data, respectively. A test statistic module generates a test statistic based on a difference between the first set of model coefficients and the second set of model coefficients. A critical value module calculates critical value for the test statistic. A hypothesis testing module compares the test statistic with the critical value using a statistical hypothesis test to determine whether the predictive model has changed. In response to a determination that the predictive model has changed, a fault indication may be generated and/or the predictive model may be adaptively updated.