Abstract: An energy cost optimization system for a building includes HVAC equipment and a controller. The controller is configured to generate a cost function defining a cost of operating the HVAC equipment as a function of one or more energy load setpoints. The controller is configured to modify the cost function to account for both an initial purchase cost of a new asset to be added to the HVAC equipment and an effect of the new asset on the cost of operating the HVAC equipment. Both the initial purchase cost of the new asset and the effect of the new asset on the cost of operating the HVAC equipment are functions of one or more asset size variables. The controller is configured to perform an optimization using the modified cost function to determine optimal values for decision variables including the energy load setpoints and the asset size variables.

Abstract: An energy storage system includes a battery and an energy storage controller. The battery is configured to store electrical energy purchased from a utility and to discharge the stored electrical energy for use in satisfying a building energy load. The energy storage controller is configured to generate a cost function including multiple demand charges. Each of the demand charges corresponds to a demand charge period and defines a cost based on a maximum amount of the electrical energy purchased from the utility during any time step within the corresponding demand charge period. The controller is configured to modify the cost function by applying a demand charge mask to each of the multiple demand charges. The demand charge masks cause the controller to disregard the electrical energy purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charge.

Abstract: An energy storage system for a building includes a battery and an energy storage controller. The battery is configured to store electrical energy purchased from a utility and to discharge stored electrical energy for use in satisfying a building energy load. The energy storage controller is configured to generate a cost function including a peak load contribution (PLC) term. The PLC term represents a cost based on electrical energy purchased from the utility during coincidental peak hours in an optimization period. The controller is configured to modify the cost function by applying a peak hours mask to the PLC term. The peak hours mask identifies one or more hours in the optimization period as projected peak hours and causes the energy storage controller to disregard the electrical energy purchased from the utility during any hours not identified as projected peak hours when calculating a value for the PLC term.

Abstract: An optimization controller for a battery includes a high level controller configured to receive a regulation signal from an incentive provider at a data fusion module, determine statistics of the regulation signal, and use the statistics of the regulation signal to generate a frequency response midpoint. The optimization controller further includes a low level controller configured to use the frequency response midpoint to determine optimal battery power setpoints and use the optimal battery power setpoints to control an amount of electric power stored or discharged from the battery during a frequency response period.

Abstract: A frequency response optimization system includes a battery configured to store and discharge electric power, a power inverter configured to control an amount of the electric power stored or discharged from the battery, a high level controller, and a low level controller. The high level controller is configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, and use the statistics of the regulation signal to generate an optimal frequency response midpoint. The optimal midpoint achieves a desired change in the state-of-charge of the battery while participating in a frequency response program. The low level controller is configured to use the midpoints to determine optimal battery power setpoints for the power inverter. The power inverter is configured to use the optimal battery power setpoints to control an amount of the electric power stored or discharged from the battery.

Abstract: A frequency regulation and ramp rate control system includes a battery configured to store and discharge electric power, a battery power inverter configured to control an amount of the electric power in the battery, a photovoltaic power inverter configured to control an electric power output of a photovoltaic field, and a controller. The controller generates a battery power setpoint for the battery power inverter and a photovoltaic power setpoint for the photovoltaic power inverter. The generated setpoints cause the battery power inverter and the photovoltaic power inverter to simultaneously perform both frequency regulation and ramp rate control.

Abstract: A frequency response controller includes a high level controller configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, and use the statistics of the regulation signal to generate a frequency response midpoint. The controller further includes a low level controller configured to use the frequency response midpoint to determine optimal battery power setpoints and use the optimal battery power setpoints to control an amount of electric power stored or discharged from a battery during a frequency response period.

Abstract: A frequency response optimization system includes a battery configured to store and discharge electric power, a power inverter configured to control an amount of the electric power stored or discharged from the battery, and a frequency response controller. The frequency response controller includes receiving a regulation signal from an incentive provider, determining statistics of the regulation signal, using the statistics of the regulation signal to generate a frequency response midpoint, and using the frequency response midpoint to determine optimal battery power setpoints for the power inverter. The power inverter is configured to use the optimal battery power setpoints to control the amount of the electric power stored or discharged from the battery.

Abstract: A power control system includes a battery, a battery power inverter configured to control an amount of the electric power stored or discharged from the battery, a photovoltaic power inverter configured to control a power output of a photovoltaic field, and a controller. The power outputs of the battery power inverter and the photovoltaic power inverter combine at a point of interconnection. The controller adjusts a setpoint for the photovoltaic power inverter in response to a determination that the total power at the point of interconnection exceeds a point of interconnection power limit.

Abstract: A frequency response optimization system includes a battery configured to store and discharge electric power, a power inverter configured to control an amount of the electric power stored or discharged from the battery at each of a plurality of time steps during a frequency response period, and a frequency response controller. The frequency response controller is configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, use the statistics of the regulation signal to generate an optimal frequency response midpoint that achieves a desired change in a state-of-charge (SOC) of the battery while participating in a frequency response program, and use the midpoints to determine optimal battery power setpoints for the power inverter. The power inverter is configured to use the optimal battery power setpoints to control the amount of the electric power stored or discharged from the battery.

Abstract: A predictive power control system includes a battery configured to store and discharge electric power, a battery power inverter configured to control an amount of the electric power stored or discharged from the battery, and a controller. The controller is configured to predict a power output of a photovoltaic field and use the predicted power output of the photovoltaic field to determine a setpoint for the battery power inverter.

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: 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 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: 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: 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: A fault parameter of an energy consumption model is modulated. The energy consumption model is used to estimate an amount of energy consumption at various values of the fault parameter. A first set of variables is generated including differences between a target value of the fault parameter and the various values of the fault parameter. A second set of variables is generated including differences between an estimated amount of energy consumption with the fault parameter at the target value and the estimated amounts of energy consumption with the fault parameter at the various values. The first set of variables and second set of variables are used to develop a regression model for the fault parameter. The regression model estimates a change in energy consumption based on a change in the fault parameter. Regression models are developed for multiple fault parameters and used to prioritize faults.

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.