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:
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:
An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The high level optimization module is configured to optimize the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment.
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 controller for performing automated system identification. The controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including generating a predictive model to predict system dynamics of a space of a building based on environmental condition inputs and including performing an optimization of a cost function of operating building equipment over a time duration to determine a setpoint for the building equipment. The optimization is performed based on the predictive model. The operations include operating the building equipment based on the setpoint to affect a variable state or condition of the space and include monitoring prediction error metrics over time. The operations include, in response to detecting one of the prediction error metrics exceeds a threshold value, updating the predictive model.
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 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 model predictive maintenance (MPM) system for building equipment. The MPM system includes an equipment controller configured to operate the building equipment to affect a variable state or condition in a building. The MPM system includes an operational cost predictor configured to predict a cost of operating the building equipment over a duration of an optimization period. The MPM system includes a budget manager configured to generate one or more budget constraints. The MPM system includes an objective function optimizer configured to optimize an objective function subject to the one or more budget constraints to determine a maintenance and replacement schedule for the building equipment. The objective function includes maintenance and replacement costs of the building equipment and the predicted cost of operating the building equipment.
Abstract:
A method for controlling equipment includes grouping a plurality of granular assets to form one or more general assets and generating models of the general assets based on the granular assets that form the general assets. Each model corresponds to a general asset and defines a relationship between resources produced by the general asset and resources consumed by the general asset. The method includes performing a first control process using the models of the general assets to determine a first allocation of resources among the general assets. The first allocation defines amounts of the resources to be consumed, produced, or stored by each of the general assets. The method includes operating the equipment to consume, produce, or store the resources in accordance with the first allocation.
Abstract:
A method for performing model predictive maintenance (MPM) of building equipment includes obtaining a first objective function that defines a cost of operating the building equipment and at least one of replacing the building equipment or performing maintenance on the building equipment as a function of operating decisions and at least one of replacement decisions or maintenance decisions for the building equipment for multiple short-term time steps within a short-term horizon. The method also includes performing a first optimization of the first objective function to generate a short-term maintenance and replacement schedule for the building equipment over a duration of the short-term horizon. The method also includes using a result of the first optimization to perform a second optimization of a second objective function to generate a long-term maintenance and replacement schedule for the building equipment over a duration of a long-term horizon.