Abstract: Predictor variables that affect production or consumption of a resource are sampled at a plurality of times within a time period and aggregated to generate an aggregated value for each predictor variable over the time period. A model is generated which estimates the production or consumption in terms of the predictor variables. A regression process is performed to generate values for a plurality of regression coefficients in the model based on a cumulative production or consumption of the resource for the time period and the aggregated values. The sampled values of the predictor variables are then applied as inputs to the model to estimate productions or consumptions of the resource at each of the plurality of times. The estimated productions or consumptions may be used as inputs to a controller that operates equipment.

Abstract: Systems and methods of generating performance index for connected equipment in a building management system are provided. The connected equipment measures monitored variables and generates status codes. The system obtains data points of the monitored variables and the status codes from past N time units, and connected equipment specific parameters. The system performs first performance checks for the connected equipment using the status codes from the past N time units. The system performs second performance checks using the data points of the monitored variables from the past N time units, the connected equipment specific parameters, and a set of predetermined rules. The system determines individual performance check indicators based on the first performance checks and the second performance checks using first weights each determined based on a different timing. The system generates an overall performance index for the connected equipment using the individual performance check indicators and second weights.

Abstract: A method of generating a fault determination in a building management system (BMS), the method including receiving signal data, generating, using a number of fault detection models, a number of fault indications based on the signal data, generating, using a weighting function, based on the number of fault indications, a fault score, comparing the fault score to a fault value, and determining, based on the comparison, an existence of a fault.

Abstract: A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause the one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a first plurality of times within a first time period; analyze cumulative sum values associated with a second plurality of times occurring before the first time to identify a second time of the second plurality of times at which a second cumulative sum value is at a local minimum; and determine that a first fault began at the second time.

Abstract: A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a plurality of times within a first time period; determine a first time at which a first cumulative sum value is at a first maximum; identify a second cumulative sum value at a second maximum at a second time occurring after the first time; compare the identified second cumulative sum value to a threshold; and based on determining that the identified second cumulative sum value does not exceed the threshold, determine that a first fault ended at the first time.

Abstract: Systems and methods of generating performance index for connected equipment in a building management system are provided. The connected equipment measures monitored variables and generates status codes. The system obtains data points of the monitored variables and the status codes from past N time units, and connected equipment specific parameters. The system performs first performance checks for the connected equipment using the status codes from the past N time units. The system performs second performance checks using the data points of the monitored variables from the past N time units, the connected equipment specific parameters, and a set of predetermined rules. The system determines individual performance check indicators based on the first performance checks and the second performance checks using first weights each determined based on a different timing. The system generates an overall performance index for the connected equipment using the individual performance check indicators and second weights.

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 chiller fault prediction system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to receive chiller data for a plurality of chillers, the chiller data indicating performance of the plurality of chillers. The one or more processors are configured to generate, based on the received chiller data, a plurality of single chiller prediction models and a plurality of cluster chiller prediction models, the plurality of single chiller prediction models generated for each the plurality of chillers and the plurality of cluster chiller prediction models generated for chiller clusters of the plurality of chillers.

Abstract: A Building Management System (BMS) is configured to monitor and control building equipment. The BMS includes a number of sensors configured to transmit input data associated with the building equipment. The BMS further includes a condition based maintenance scheduler configured to determine a number of potential problems associated with the building equipment based on the input data. The condition based maintenance scheduler determines the number of potential problems by comparing the input data to a set of conditions. The BMS is further configured to schedule a number of maintenance events associated with the building equipment based on the number of potential problems.

Abstract: Predictor variables that affect a building energy load are sampled at a plurality of times within a time period and aggregated to generate an aggregated value for each predictor variable over the time period. A model is generated which estimates the building energy load in terms of the predictor variables. A regression process is performed to generate values for a plurality of regression coefficients in the model based on a cumulative building energy load for the time period and the aggregated values. The sampled values of the predictor variables are then applied as inputs to the model to estimate building energy loads at each of the plurality of times. The estimated building energy loads may be used as inputs to a controller that optimizes operation of a central plant.

Abstract: Predictor variables that affect production or consumption of a resource are sampled at a plurality of times within a time period and aggregated to generate an aggregated value for each predictor variable over the time period. A model is generated which estimates the production or consumption in terms of the predictor variables. A regression process is performed to generate values for a plurality of regression coefficients in the model based on a cumulative production or consumption of the resource for the time period and the aggregated values. The sampled values of the predictor variables are then applied as inputs to the model to estimate productions or consumptions of the resource at each of the plurality of times. The estimated productions or consumptions may be used as inputs to a controller that operates equipment.

Abstract: The present disclosure is directed to a method for performing energy analytics in a building management system. The method can include collecting respective data samples of one or more variables from building equipment during a first period of time and a second period of time. The method can include calculating a first plurality of values for one or more energy audit metrics based on the data samples collected during the first period of time and the second period of time. The method can include comparing the first plurality of values and second plurality of values. The method can include displaying, based on the comparison, at least one of the first plurality of values and/or at least one of the second plurality of values on a dashboard to facilitate adjustment of the one or move 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 determine an average of a minimum half of sorted energy consumption values for a first time period and determine an average of a maximum half of sorted energy consumption values for a second time period. The instructions also cause the processor to determine a ratio of the average of the minimum half of sorted energy consumption values for the first time period to the average of the maximum half of sorted energy consumption values for the second time period, compare the calculated ratio to an adaptively tunable threshold value and activate a system responsive to the calculated ratio exceeding the adaptively tunable threshold value.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment, a controller, and a predictive modeling system. The HVAC equipment are operable to affect an environmental condition in the building. The controller is configured to determine an operating point for the HVAC equipment and to operate the HVAC equipment at the operating point. The predictive modeling system includes a plurality of HVAC component models and one or more prediction combiners. The HVAC component models are configured to generate a plurality of component model predictions based on the operating point. The prediction combiners are configured to combine the plurality of component model predictions to form a combined model prediction. The controller is configured to use the combined model prediction to optimize the operating point and to operate the HVAC equipment at the optimized operating point.

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 heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment, a controller, and a predictive modeling system. The HVAC equipment are operable to affect an environmental condition in the building. The controller is configured to determine an operating point for the HVAC equipment and to operate the HVAC equipment at the operating point. The predictive modeling system includes a plurality of HVAC component models and one or more prediction combiners. The HVAC component models are configured to generate a plurality of component model predictions based on the operating point. The prediction combiners are configured to combine the plurality of component model predictions to form a combined model prediction. The controller is configured to use the combined model prediction to optimize the operating point and to operate the HVAC equipment at the optimized operating point.

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.