Abstract: A method for controlling flow in a heating, ventilation, and air conditioning (HVAC) system that imposes an upper limit on the flow of fluid through a heating or cooling coil. Imposing this limit on the flow rate ensures that a temperature change across the coil remains above a minimum threshold and can significantly reduce energy waste. The method includes receiving a first temperature measurement associated with an inlet of the coil, receiving a second temperature measurement associated with an outlet of the coil, and receiving a flow measurement associated with the valve, applying the first temperature measurement, the second temperature measurement, and the flow measurement as input to a model, determining a maximum flow rate that ensures that a difference between the first temperature measurement and the second temperature measurement is above a threshold using the model, and operating the valve in accordance with the maximum flow rate.

Abstract: A method for controlling flow in a heating, ventilation, and air conditioning (HVAC) system includes adjusting a setpoint associated with a valve based on a temperature change across a heating or cooling coil to reduce energy waste. The method includes receiving a first temperature measurement associated with an inlet of the coil, receiving a second temperature measurement associated with an outlet of the coil, calculating a difference between the first temperature measurement and the second temperature measurement, determining that the difference between the first temperature measurement and the second temperature measurement is below a threshold, and adjusting a setpoint associated with the valve. Additional control features may be provided to improve system behavior such as a pulse generation feature, a change-limiting feature, and a reevaluation feature.

Abstract: A control system includes one or more sensors configured to generate sensor data relating to building equipment. The sensor data includes first sensor data collected during a first time period and second sensor data collected during a second time period. The control system also includes a controller configured to use the first sensor data to generate one or more first model coefficients for a model for the building equipment. The controller is also configured to use the second sensor data to generate one or more second model coefficients for the model, generate a test statistic by comparing the one or more first model coefficients and the one or more second model coefficients, compare the test statistic with a value, and automatically identify a change relating to the building equipment in response to the test statistic exceeding the value.

Abstract: An electrical energy storage system includes a battery configured to store and discharge electric power to an energy grid, a power inverter configured to use battery power setpoints to control an amount of the electric power stored or discharged from the battery, the battery power setpoints comprising at least one of frequency regulation power setpoints and ramp rate control power setpoints, and a controller. The controller is configured to use a battery life model to generate the battery power setpoints for the power inverter. The battery life model includes one or more variables that depend on the battery power setpoints.

Abstract: A heating, ventilation, or air conditioning system (HVAC) design and operational tool includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including obtaining a dynamic temperature model and a dynamic infectious quanta model for one or more building zones, determining an infection probability, and performing a plurality of simulations for a plurality of different equipment configurations using the dynamic temperature model, the dynamic infectious quanta model, and the infection probability to generate results. The operations include generating, using the results of the plurality of simulations, at least one of design including one or more recommended design parameters data or operational data including one or more recommended operational parameters for the HVAC system and initiating an automated action using at least one of the design data or the operational data.

Abstract: Systems and methods for monitoring and controlling a plant using extremum-seeking control. The method includes perturbing a setpoint for a controller by applying a dither signal to the setpoint. The controller uses a perturbed setpoint to generate one or more control inputs for the building equipment. Receiving, from the building equipment, an output signal and obtaining values of a performance variable based on the output signal, the values of the performance variable resulting from operating the building equipment based on the perturbed setpoint. The method includes determining a gradient of the performance variable with respect to the perturbed setpoint and a Hessian of the performance variable affected by the building equipment with respect to the setpoint. The method includes determining an adjustment to the setpoint predicted to drive the performance variable to an extremum based on the gradient and the Hessian of the performance variable.

Abstract: Systems and methods for monitoring and controlling a plant using extremum-seeking control. The method includes perturbing a setpoint for a controller by applying a dither signal to the setpoint. The controller uses a perturbed setpoint to generate one or more control inputs for the building equipment. Receiving, from the building equipment, an output signal and obtaining values of a performance variable based on the output signal, the values of the performance variable resulting from operating the building equipment based on the perturbed setpoint. The method includes determining a gradient of the performance variable with respect to the perturbed setpoint and a Hessian of the performance variable affected by the building equipment with respect to the setpoint. The method includes determining an adjustment to the setpoint predicted to drive the performance variable to an extremum based on the gradient and the Hessian of the performance variable.

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: An energy storage system includes a photovoltaic energy field, a stationary energy storage device, an energy converter, and a controller. The photovoltaic energy field converts solar energy into electrical energy and charges the stationary energy storage device with the electrical energy. The energy converter converts the electrical energy stored in the stationary energy storage device into AC power at a discharge rate and supplies a campus with the AC power at the discharge rate. The controller predicts a required load of the campus and an electrical generation of the photovoltaic energy field across a time horizon and optimizes a cost function subject to a set of constraints to determine a discharge rate of the AC power to achieve a desired power factor. At least one of the set of constraints applied to the cost function ensures that the energy converter can convert the electrical energy stored in the stationary energy storage device into AC power having the determined power factor and discharge rate.

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 building energy system includes equipment and an asset allocator configured to determine an optimal allocation of energy loads across the equipment over a prediction horizon. The asset allocator generates several potential scenarios and generates an individual cost function for each potential scenario. Each potential scenario includes a predicted load required by the building and predicted prices for input resources. Each individual cost function includes a cost of purchasing the input resources from utility suppliers. The asset allocator generates a resource balance constraint and solves an optimization problem to determine the optimal allocation of the energy loads across the equipment. Solving the optimization problem includes optimizing an overall cost function that includes a weighted sum of individual cost functions for each potential scenario subject to the resource balance constraint for each potential scenario.

Abstract: A method for operating HVAC equipment includes obtaining building data for each of a plurality of time steps. The building data relates to resource usage of HVAC equipment. The method also includes calculating a recursive residual for each of a plurality of overlapping time periods using the building data. Each overlapping time period includes a subset of the plurality of time steps. The method also includes, for each of the plurality of time steps, calculating a metric based on the recursive residuals for overlapping time periods that end on or before the time step and automatically detecting a change in static factors for one or more buildings served by the HVAC equipment by comparing the metrics for the plurality of time steps based on a statistical property of the metrics.

Abstract: A system for controlling temperature of a building space includes HVAC equipment configured to serve the building space, a plurality of sensors configured to measure a plurality of parameters relating to the building space, and a control system. The control system is configured to receive data from the plurality of sensors, generate a plurality of disturbance heat flow estimates for the building space based on the data from the plurality of sensors, determine a feedforward heat flow contribution based on the disturbance heat flow estimates, determine a feedback heat flow contribution based on a measured temperature of the building space and a temperature setpoint for the building space, combine the feedforward heat flow contribution and the feedback heat flow contribution to determine a rate of heat flow to be provided to the building space by the HVAC equipment, and control the HVAC equipment to provide the rate of heat flow.

Abstract: An energy storage system includes a photovoltaic energy field, a stationary energy storage device, an energy converter, and a controller. The photovoltaic energy field converts solar energy into electrical energy and charges the stationary energy storage device with the electrical energy. The energy converter converts the electrical energy stored in the stationary energy storage device into AC power at a discharge rate and supplies a campus with the AC power at the discharge rate. The controller generates a cost function of the energy consumption of the campus across a time horizon which relates a cost to operate the campus to the discharge rate of the AC power supplied by the stationary energy storage device. The controller applies constraints to the cost function, determines a minimizing solution to the cost function which satisfies the constraints, and controls the energy converter.

Abstract: A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, the building device data comprising a plurality of data samples of a data point and generate a time correlated data stream for the data point, the time correlated data stream comprising values of the plurality of data samples of the data point. The instructions cause the one or more processors to generate a time correlated reliability data stream for the data point, the time correlated reliability data stream comprising a plurality of reliability values indicating reliability of the values of the plurality of data samples of the data point.

Abstract: A building system of a building including one or more memory devices configured to store one or more instructions that, when executed on one or more processors, cause the one or more processors to exercise a building entity causing building entity data to be generated associated with the building entity, the building entity data indicating a result of exercising the building entity and collect the building entity data. The instructions cause the one or more processors to identify, based on a relational model, one or more relationships between one or more building entities and the building entity, wherein the one or more relationships indicate that exercising the building entity affects operation of the one or more building entities and identify that the building is experiencing a performance issue by analyzing the building entity data and the one or more relationships.

Abstract: A building control system has a controller including a processing circuit configured to transition between multiple discrete operating states by executing state transitions, generate an adaptive user interface including a representation of the multiple discrete operating states and the state transitions therebetween, and automatically learn one or more learned behaviors or preferences of the user. The processing circuit is also configured to retrieve, from a descriptive text database, a description of a selected operating state or selected state transition based on the one or more learned behaviors or preferences of the user, the descriptive text database storing multiple different descriptions of the selected operating state or selected state transition, and update the adaptive user interface to provide the description of the selected operating state or selected state transition retrieved from the descriptive text database based on the one or more learned behaviors or preferences of the user.

Abstract: An energy storage system includes a photovoltaic energy field, a stationary energy storage device, an energy converter, and a controller. The photovoltaic energy field converts solar energy into electrical energy and charges the stationary energy storage device with the electrical energy. The energy converter converts the electrical energy stored in the stationary energy storage device into AC power at a discharge rate and supplies a campus with the AC power at the discharge rate. The controller generates a cost function of the energy consumption of the campus across a time horizon which relates a cost to operate the campus to the discharge rate of the AC power supplied by the stationary energy storage device. The controller applies constraints to the cost function, determines a minimizing solution to the cost function which satisfies the constraints, and controls the energy converter.

Abstract: An energy storage system includes a photovoltaic energy field, a stationary energy storage device, an energy converter, and a controller. The photovoltaic energy field converts solar energy into electrical energy and charges the stationary energy storage device with the electrical energy. The energy converter converts the electrical energy stored in the stationary energy storage device into AC power at a discharge rate and supplies a campus with the AC power at the discharge rate. The controller predicts a required load of the campus and an electrical generation of the photovoltaic energy field across a time horizon and optimizes a cost function subject to a set of constraints to determine a discharge rate of the AC power to achieve a desired power factor. At least one of the set of constraints applied to the cost function ensures that the energy converter can convert the electrical energy stored in the stationary energy storage device into AC power having the determined power factor and discharge rate.

Abstract: A space controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include obtaining a power consumption model that defines a change in power consumption of HVAC equipment that operate to provide heating or cooling to a space as a function of a change in temperature setpoint for the space and model parameters that represent thermal properties of the space. The operations include estimating values of the model parameters based on training data including values of the power consumption of the HVAC equipment, the temperature setpoint for the space, and outdoor air temperature at multiple times within a training period. The operations include using the power consumption model and the values of the model parameters to predict a change in the power consumption of the HVAC equipment expected to result from a change in the temperature setpoint.