Abstract: Systems and methods for determining an appropriate parameter order for a building energy use model are provided. A described method includes receiving an energy use model for a building site, obtaining a plurality of data points, calculating a first regression statistic indicating a fit of the energy use model to the plurality of data points under a null hypothesis and a second regression statistic indicating a fit of the energy use model to the plurality of data points under an alternative hypothesis, and comparing a test statistic to a threshold value. The test statistic is a function of the first regression statistic and the second regression statistic. The method further includes determining an appropriate parameter order for the energy use model based on a result of the comparison.

Abstract: Methods for system identification are presented using model predictive control to frame a gray-box parameterized state space model. System parameters are identified using an optimization procedure to minimize a first error cost function within a range of filtered training data. Disturbances are accounted for using an implicit integrator within the system model, as well as a parameterized Kalman gain. Kalman gain parameters are identified using an optimization procedure to minimize a second error cost function within a range of non-filtered training data. Recursive identification methods are presented to provide model adaptability using an extended Kalman filter to estimate model parameters and a Kalman gain to estimate system states.

Abstract: A set of energy use model parameters for each of a plurality of buildings is used to determine a typical set of energy use model parameters for the plurality of buildings. A distance between the typical set of energy use model parameters and the set of energy use model parameters for each of the plurality of buildings is determined. Each distance is compared to a critical value. A building is identified as a candidate for energy conservation measures in response to the distance for the building exceeding the critical value. Energy conservation measures are implemented in the identified building. Implementing energy conservation measures may include replacing existing HVAC equipment in the identified building with new energy-efficient HVAC equipment.

Abstract: A computer system for use with a building management system for a building includes a processing circuit configured to automatically identify a change in a building's energy usage model based on data received from the building management system. The processing circuit may be configured to communicate the identified change in the static factor to at least one of (a) a module for alerting a user to the identified change and (b) a module for initiating an adjustment to the energy model for a building in response to the identified change.

Abstract: A method of maintaining a temperature of a surface in a building space to be above a dew point of the building space includes calculating a dew point based on a temperature measurement and a relative humidity measurement. The method further includes estimating a safety margin associated with the dew point calculation, determining a minimum temperature of the surface, wherein the minimum temperature is a function of the dew point and the safety margin, and commanding a first component of a building automation system to adjust a current temperature of the surface to be greater than the minimum temperature.

Abstract: A building management system (BMS) includes sensors that measure time series values of building variables and a deterministic model generator that uses historical values for the time series of building variables to train a deterministic model that predicts deterministic values for the time series. The BMS includes a stochastic model generator that uses differences between actual values for the time series and the predicted deterministic values to train a stochastic model that predicts a stochastic value for the time series. The BMS includes a forecast adjuster that adjusts the predicted deterministic values using the predicted stochastic value to generate an adjusted forecast for the time series. The BMS includes a demand response optimizer that uses the adjusted forecast to generate an optimal set of control actions for building equipment of the BMS. The building equipment operate to affect the building variables.

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: 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 the optimization period as a function of the utility rate data and an amount of the one or more resources consumed by multiple groups of the central plant equipment. The optimization system includes a load change penalty module configured to modify the objective function to account for a load change penalty resulting from a change in an amount of the building energy loads assigned to one or more of the groups of central plant equipment.

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 optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.

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 using an energy use model of a building for benchmarking is shown and described. An exemplary method includes receiving building data indicative of one or more characteristics of the building. The method further includes generating an energy use model for the building based on the building data and using the energy use model to generate statistics for the building. The method also includes identifying one or more other buildings having the same classification as the building. The method yet further includes comparing the generated statistics for the building to statistics for the identified one or more other buildings and providing an indication of the comparison.

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: Systems and methods for determining an appropriate parameter order for a building energy use model are provided. A described method includes receiving an energy use model for a building site, obtaining a plurality of data points, calculating a first regression statistic indicating a fit of the energy use model to the plurality of data points under a null hypothesis and a second regression statistic indicating a fit of the energy use model to the plurality of data points under an alternative hypothesis, and comparing a test statistic to a threshold value. The test statistic is a function of the first regression statistic and the second regression statistic. The method further includes determining an appropriate parameter order for the energy use model based on a result of the comparison.

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: 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's energy consumption may be modeled using weather data, utility billing data, or other data regarding the building. The resulting model data may be analyzed to detect a shift in the model data, which may indicate the presence of a fault condition. Changes to the model's coefficients that would result from an upgrade, energy conservation measure, or other action may also be used to predict the resulting Energy Star score for 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 mathematical linear operator is found that transforms the unused or deferred cooling power usage of the HVAC system based on pre-determined temperature settings to a target cooling power usage. The mathematical operator is applied to the temperature settings to create a temperature setpoint trajectory expected to provide the target cooling power usage.

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 limiting power consumption by a heating, ventilation, and air conditioning (HVAC) subsystem of a building are shown and described. A mathematical linear operator is found that transforms the unused or deferred cooling power usage of the HVAC system based on pre-determined temperature settings to a target cooling power usage. The mathematical operator is applied to the temperature settings to create a temperature setpoint trajectory expected to provide the target cooling power usage.

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.