Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.

Abstract: A method for automatically commissioning and operating an HVAC system to serve energy loads of a building site is shown. The method includes querying site information describing the building site to identify physical equipment and relationships between the physical equipment. The method further includes constructing an asset allocator model, the asset allocator model indicating connections between the physical equipment and resources produced or consumed by the physical equipment. The method further includes generating a mapping between points of the physical equipment at the building site and corresponding variables of the asset allocator model. The method further includes using the asset allocator model to generate values of one or more control variables of the asset allocator model. The method further includes adjusting an operation of the physical equipment by triggering software elements to automatically push updated values of the control variables to corresponding points of the physical equipment.

Abstract: A control system for a central plant having subplants including devices operating to serve energy loads of a building. The system includes a high level optimization module that performs high level optimization of thermal loads subject to constraints to generate subplant load allocations. The control system includes a low level optimization module that performs low level optimization of the subplant load allocations to determine operating states for the devices. The control system includes a constraint modifier that modifies the constraints for the high level optimization module based on equipment schedules. The control system also includes a binary optimization modifier including a pruner module that receives the minimum off schedule to determine adjusted branches and a seeder module that receives the minimum on schedule to determine a starting node for use in binary optimization performed by the low optimization module.

Abstract: A building management system (BMS) for filtering a fluid within a building is shown. The system includes one or more sensors configured to measure one or more characteristics of a first fluid within an air duct of the BMS and measure one or more characteristics of a second fluid after the second fluid has been filtered. The system further includes a pollutant management system configured to receive data from the one or more sensors and control a filtration process. The filtration process selects a filter of a plurality of filters based on a level of the one or more characteristics of the first fluid and the one or more characteristics of the second fluid.

Abstract: A system includes a plurality of thermostats corresponding to a plurality of HVAC systems that serve a plurality of spaces and a computing system communicable with the plurality of thermostats via a network. The computing system is configured to, for each space of the plurality of spaces, obtain a set of training data relating to thermal behavior of the space, identify a model of thermal behavior of the space based on the set of training data, perform a model predictive control process using the model of thermal behavior of the space to obtain a temperature setpoint for the space, and provide the temperature setpoint to the thermostat corresponding to the HVAC system serving the space. The plurality of thermostats are configured to control the plurality of HVAC systems in accordance with the temperature setpoints.

Abstract: Systems and methods for implementing an economic strategy such as a model predictive control (EMPC) strategy. An EMPC tool is configured to present to receive sinks and connections between central plant equipment. The EMPC tool also includes a data model extender configured to extend a data model to define new entities and/or relationships. The EMPC tool also includes a high level EMPC algorithm configured to generate an optimization problem and an asset allocator configured to solve the resource optimization problem in order to determine optimal control decisions used to operate the central plant.

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 method for automatically commissioning and operating an HVAC system to serve energy loads of a building site is shown. The method includes querying site information describing the building site to identify physical equipment and relationships between the physical equipment. The method further includes constructing an asset allocator model, the asset allocator model indicating connections between the physical equipment and resources produced or consumed by the physical equipment. The method further includes generating a mapping between points of the physical equipment at the building site and corresponding variables of the asset allocator model. The method further includes using the asset allocator model to generate values of one or more control variables of the asset allocator model. The method further includes adjusting an operation of the physical equipment by triggering software elements to automatically push updated values of the control variables to corresponding points of the physical equipment.

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 optimization controller for a battery includes a high level controller configured to receive a regulation signal from an incentive provider cat 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 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 method includes determining control setpoints for equipment based on a time-varying availability of green energy and revenue from an incentive program of an energy provider. The method also includes controlling the equipment using the control setpoints.

Abstract: A controller for a building control system includes processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations including identifying zones within a building, analyzing data associated with the zones, and generating zone groupings based on the data associated with the zones. Each of the zone groupings define zone groups and specify which of the zones are grouped together to form each of the zone groups. The operations also include identifying a particular zone grouping from zone groupings based on the data associated with zones and using the particular zone grouping to generate control signals to operate equipment of the building control system to provide heating or cooling to the zones.

Abstract: A building management system includes building equipment operable to affect a variable state or condition of a building and a control system configured to receive a user input indicating a model form. The model form includes a plurality of matrices having a plurality of elements defined in terms of a plurality of parameters. The control system is configured to parse the model form to generate a sequence of machine-executable steps for determining a value of each of the plurality of elements based on a set of potential parameter values, identify a system model by executing the sequence of machine-executable steps to generate a set of parameter values for the plurality of parameters, generate a graphical user interface that illustrates a fit between predictions of the identified system model and behavior of the variable state or condition of the building, and control the building equipment using the identified system model.

Abstract: A method for detecting faults in a building management system (BMS) is shown. The method includes receiving time series data characterizing an operating performance of one or more BMS devices. The method further includes processing the time series data using multiple different fault detection methods to generate multiple fault detection results. The method includes providing the multiple fault detection results as outputs from the multiple different fault detection methods. The method includes applying the multiple fault detection results as inputs to a neural network that determines whether the multiple fault detection results are indicative of a fault condition in the BMS.

Abstract: A method includes operating equipment in accordance with a setpoint to affect a measurement for a space during a training period, performing a plurality of assessments of different characteristics of data for a segment of the training period, and training a system model using a set of training data. The data for the segment is included in the set of training data in response to passing the plurality of assessments or excluded from the set of training data in response to failing one or more of the plurality of assessments. The method also includes controlling the equipment using the system model.

Abstract: A method for automatically adapting a predictive model used to control a heating, ventilation, or air conditioning (HVAC) system in a building to compensate for a detected fault in the HVAC system is shown. The method includes obtaining an indication of the detected fault in the HVAC system or a zone in the building. The method further includes determining a predicted impact of the detected fault on an operational performance of the HVAC system. The method further includes adjusting one or more parameters of the predictive model based on the predicted impact of the detected fault to generate a fault-adapted predictive model. The method further includes operating the HVAC system to control an environmental condition of the building using the fault-adapted predictive model.

Abstract: A controller for operating building equipment of a building. The controller includes one or more processors. The controller includes one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include comparing one or more model parameters of predictive models describing zones of the building to determine one or more zone groups for the building. The operations include generating one or more zone group models corresponding to the one or more zone groups. The operations include operating the building equipment using the one or more zone group models to affect a variable state or condition of the building.

Abstract: A controller is configured to obtain a cost function defining a cost of operating building equipment over a time period. The cost function includes a revenue term defining revenue to be obtained by operating the equipment to participate in an incentive program over the time period. The controller is configured to modify the cost function to account for an initial purchase cost of a new asset to be added to the equipment and an effect of the new asset on the cost of operating the equipment. The initial purchase cost of the new asset and the effect of the new asset on the cost of operating the equipment are functions of asset size variables. The controller is also configured to perform an optimization of the modified cost function to determine values for energy load setpoints, the asset size variables, and participation in the incentive program over the time period.

Abstract: A system controlling equipment to serve energy loads of a building includes a cost function generator configured to obtain a cost function of decision variables representing an amount of resources consumed or produced by the equipment, an optimizer configured to perform a first optimization of the cost function subject to a first set of constraints defining first values of constraint variables to generate a first result defining first values of decision variables, a constraint modifier configured determine a cost gradient, recommend changes to constraint variables, and modify constraint variables to modified values in response to changes. The optimizer is configured to perform an optimization of the cost function subject to a second set of constraints to generate a second optimization result defining second values of the decision variables. The system includes a controller that operates equipment to consume or produce resources defined by second values of the decision variables.