Abstract: A building management system (BMS) includes a baseline model generator configured to receive an initial set of predictor variables for potential use in an energy usage model for a building, generate a first set of coefficients for the baseline energy usage model based on the initial set of predictor variables, remove one of the predictor variables from the initial set of predictor variables to create a subset of the initial set of predictor variables, generate a second set of coefficients for the baseline energy usage model based on the subset of the initial set of predictor variables, calculate a test statistic for the removed variable using a difference between the first set of coefficients and the second set of coefficients, and automatically select the removed predictor variable for use in the baseline energy usage model in response the test statistic exceeding a critical value.

Abstract: A system for measuring and verifying energy savings resulting from energy conservation measures in a building includes one or more energy meters and a controller. The energy meters are configured to measure an actual amount of building energy usage The controller is configured to determine an actual amount of energy savings resulting from the energy conservation measures during the measurement and verification period and to calculate a least amount of energy savings resulting from the energy conservation measures. The least amount of energy savings is a lower confidence bound on the actual amount of energy savings. The controller is configured to cause a building management system for the building to stop performing a measurement and verification process before a normal end of the measurement and verification period in response to the least amount being greater than a target amount of energy savings to be achieved by the energy conservation measures.

Abstract: A central plant includes an electrical energy storage subplant configured to store electrical energy, a plurality of generator subplants configured to consume one or more input resources, including discharged electrical energy, and a controller. The controller is configured to determine, for each time step within a time horizon, an optimal allocation of the input resources. The controller is configured to determine optimal allocation of the output resources for each of the subplants in order to optimize a total monetary value of operating the central plant over the time horizon.

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: Systems and methods for evaluating a fault condition in a building include determining a change to energy use model parameters attributable to the fault condition. The change to the energy use model parameters are used to calculate a corresponding change to the building's energy consumption.

Abstract: Methods and systems to minimize energy cost in response to time-varying energy prices are presented for a variety of different pricing scenarios. A cascaded model predictive control system is disclosed comprising an inner controller and an outer controller. The inner controller controls power use using a derivative of a temperature setpoint and the outer controller controls temperature via a power setpoint or power deferral. An optimization procedure is used to minimize a cost function within a time horizon subject to temperature constraints, equality constraints, and demand charge constraints. Equality constraints are formulated using system model information and system state information whereas demand charge constraints are formulated using system state information and pricing information. A masking procedure is used to invalidate demand charge constraints for inactive pricing periods including peak, partial-peak, off-peak, critical-peak, and real-time.

Abstract: A building control system determines the uncertainty in a break even temperature parameter of an energy use model. The energy use model is used to predict energy consumption of a building site as a function of the break even temperature parameter and one or more predictor variables. The uncertainty in the break even temperature parameter is used to analyze energy performance of the building site.

Abstract: A building control system uses an empirical technique to determine the uncertainty in parameters of an energy use model. The energy use model is used to predict energy consumption of a building site as a function of the model parameters and one or more predictor variables. The empirical technique includes obtaining a set of data points, each of the data points including a value for the one or more predictor variables and an associated energy consumption value for the building site. Multiple samples are generated from the set of data points, each of the multiple samples including a plurality of data points selected from the set of data points. For each of the multiple samples, the model parameters are estimated using the plurality of data points included in the sample. The uncertainty in the model parameters is determined using the multiple estimates of the model parameters.

Abstract: A computerized method of determining the end of a measurement and verification period of a building management system is provided. The method includes calculating, for a plurality of periods, a target amount of energy savings resulting from energy conservation measures in a building. The method further includes calculating, for a plurality of periods, a least amount of energy savings resulting from energy conservation measures in a building. The method further includes comparing the target amount to the least amount. The method further includes ending the measurement and verification period, before the end of a maximum measurement and verification period, when the least amount is greater than the target amount. The method further includes outputting an indication that the measurement and verification period has ended to at least one of a memory device, a user device, or another device on the building management system.

Abstract: A controller is configured to use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller. The energy cost function includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system. The controller is configured to linearize the demand charge term by imposing demand charge constraints and to mask each of the demand charge constraints that applies to an inactive pricing period. The controller is configured to determine optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints and to provide the optimal values of the one or more setpoints to equipment of the building system that operate to affect the maximum power usage.

Abstract: A cascaded control system is configured to control power consumption of a building during a demand limiting period. The cascaded control system includes an energy use setpoint generator and a feedback controller. The energy use setpoint generator is configured to use energy pricing data and measurements of a variable condition within the building to generate an energy use setpoint during the demand limiting period. The feedback controller is configured to use a difference between the energy use setpoint and a measured energy use to generate a control signal for building equipment that operate to affect the variable condition within the building during the demand limiting period.

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 building management system (BMS) includes a baseline model generator configured to receive an initial set of predictor variables for potential use in an energy usage model for a building, generate a first set of coefficients for the baseline energy usage model based on the initial set of predictor variables, remove one of the predictor variables from the initial set of predictor variables to create a subset of the initial set of predictor variables, generate a second set of coefficients for the baseline energy usage model based on the subset of the initial set of predictor variables, calculate a test statistic for the removed variable using a difference between the first set of coefficients and the second set of coefficients, and automatically select the removed predictor variable for use in the baseline energy usage model in response the test statistic exceeding a critical value.

Abstract: Systems and methods for determining the uncertainty in parameters of a building energy use model are provided. A disclosed method includes receiving an energy use model for a building site. The energy use model includes one or more predictor variables and one or more model parameters. The method further includes calculating a gradient of an output of the energy use model with respect to the model parameters, determining a covariance matrix using the calculated gradient, and using the covariance matrix to identify an uncertainty of the model parameters. The uncertainty of the model parameters may correspond to entries in the covariance matrix.

Abstract: A method for detecting and cleansing suspect building automation system data is shown and described. The method includes using processing electronics to automatically determine which of a plurality of error detectors and which of a plurality of data cleansers to use with building automation system data. The method further includes using processing electronics to automatically detect errors in the data and cleanse the data using a subset of the error detectors and a subset of the cleansers.

Abstract: A computer system for use with a building management system in a building includes a processing circuit configured to use historical data received from the building management system to automatically select a set of variables estimated to be significant to energy usage in the building. The processing circuit is further configured to apply a regression analysis to the selected set of variables to generate a baseline model for predicting energy usage in the building.

Abstract: A computer system for use with a building management system in a building includes a processing circuit configured to use historical data received from the building management system to automatically select a set of variables estimated to be significant to energy usage in the building. The processing circuit is further configured to apply a regression analysis to the selected set of variables to generate a baseline model for predicting energy usage in the building.

Abstract: Systems and methods for limiting power consumption by a heating, ventilation, and air conditioning (HVAC) subsystem of a building are shown and described. A feedback controller is used to generate a manipulated variable based on an energy use setpoint and a measured energy use. The manipulated variable may be used for adjusting the operation of an HVAC device.

Abstract: A building analysis system includes a communications interface that receives energy consumption data for a building site including energy-consuming building equipment. A processing circuit of the building analysis system calculates first and second regression statistics indicating a fit of an energy use model to the energy consumption data under a null hypothesis that the energy use model has a first parameter order and an alternative hypothesis that the energy use model has a second parameter order different from the first parameter order. The processing circuit generates a test statistic indicating an improvement between the first regression statistic and the second regression statistic, compares the test statistic to a threshold value to determine whether the improvement warrants rejecting the null hypothesis, and determines an appropriate parameter order for the energy use model based on a result of the comparison.

Abstract: A controller for a building system receives training data that includes input data and output data. The output data measures a state of the building system affected by both the input data and an extraneous disturbance. The controller performs a two-stage optimization process to identify system parameters and Kalman gain parameters of a dynamic model for the building system. During the first stage, the controller filters the training data to remove an effect of the extraneous disturbance from the output data and uses the filtered training data to identify the system parameters. During the second stage, the controller uses the non-filtered training data to identify the Kalman gain parameters. The controller uses the dynamic model with the identified system parameters and Kalman gain parameters to generate a setpoint for the building system. The building system uses the setpoint to affect the state measured by the output data.