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 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 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 system for generating an energy use model of a building includes a processing circuit operable to receive building data indicative of a first type of building variable and to receive additional building data correlated to an energy use of the building. The processing circuit is also operable to determine a portion of the building variable that is uncorrelated with the additional building data. The processing circuit is further operable to use the additional building data and the uncorrelated portion of the building variable to generate the energy use model of the building.
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 model predictive maintenance (MPM) system for building equipment includes one or more processing circuits having one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including estimating a degradation state of the building equipment, using a degradation impact model to predict an amount of one or more input resources consumed by the building equipment to produce one or more output resources based on the degradation state of the building equipment, generating a maintenance schedule for the building equipment based on the amount of the one or more input resources predicted by the degradation impact model, and initiating a maintenance activity for the building equipment in accordance with the maintenance schedule.
Abstract:
A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.
Abstract:
A controller for equipment that operates to affect a variable state or condition of a building including one or more processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include generating a new predictive model using training data associated with one or more durations selected from a time period and selected to satisfy a set of criteria. The predictive model models system dynamics of the building during the time period. The operations include storing the new predictive model in a database including predictive models that model the system dynamics of the building and include comparing performance of the new predictive model and the predictive models stored by the database to select a particular predictive model for controlling the equipment. The operations include using the particular predictive model to generate and provide control signals to the equipment.
Abstract:
A method for controlling HVAC equipment for a building includes generating, based on historical building data, a discomfort tolerance defining an acceptable amount of occupant discomfort, determining a first value of an environmental condition at which the occupant discomfort is predicted to exceed the discomfort tolerance in a first direction, determining a second value of the environmental condition at which the occupant discomfort is predicted to exceed the discomfort tolerance in a second direction opposite the first direction, and controlling the HVAC equipment to maintain the environmental condition between the first value and the second value.