Abstract: A method for operating a subplant included in a central plant includes obtaining instruction-based equipment models associated with devices included in the subplant and comprises operating points that define an operation of the devices, generating, for each instruction-based equipment models, a geometric equipment model using the operating points from a particular instruction-based equipment model, the geometric equipment model defining at least one operating domain associated with the particular device, merging geometric equipment models to form a geometric subplant model, the geometric subplant model defining an operation of the subplant comprising devices associated with the geometric equipment models, receiving a desired operating point comprising a load value, determining, relative to the desired operating point, a nearest operating point on the geometric subplant model, setting the nearest operating point on the geometric subplant model as an actual operating point, and operating the subplant at the act

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 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: Granular assets of a building are identified. Each granular asset represents one or more devices of that operate to produce, consume, or store resources in the building. General assets are defined by grouping the granular assets into groups. A non-granular optimization is performed to determine a non-granular allocation of the resources among the general assets. Non-granular allocation defines an amount of each of the resources consumed, produced, or stored by each of the general assets at each time step in a time period. A granular optimization is performed for each general asset to determine a granular allocation of the resources among the granular assets. The granular allocation defines an amount of each resource consumed, produced, or stored by the granular assets at each time step in the time period. The equipment of the building are operated to consume, produce, or store the amount of each resource defined by granular allocation.

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.

Abstract: A method for operating a subplant included in a central plant includes obtaining instruction-based equipment models associated with devices included in the subplant and comprises operating points that define an operation of the devices, generating, for each instruction-based equipment models, a geometric equipment model using the operating points from a particular instruction-based equipment model, the geometric equipment model defining at least one operating domain associated with the particular device, merging geometric equipment models to form a geometric subplant model, the geometric subplant model defining an operation of the subplant comprising devices associated with the geometric equipment models, receiving a desired operating point comprising a load value, determining, relative to the desired operating point, a nearest operating point on the geometric subplant model, setting the nearest operating point on the geometric subplant model as an actual operating point, and operating the subplant at the act

Abstract: A method for operating equipment according to sequence of operation using geometric models including obtaining a first geometric model for a first set of equipment and a second geometric model for a second set of equipment, the first set of equipment and the second set of equipment defined by the sequence of operation for the equipment, locating, on the first geometric model, a first nearest operating point based on a desired operating point, generating a first modified geometric model by removing one or more operating points that do not satisfy the first nearest operating point, generating a merged geometric model by merging the first modified geometric model with the second geometric model, locating a second nearest operating point based on a modified desired operating point, and operating the equipment in accordance with the first nearest operating point and the second nearest operating point.

Abstract: In one aspect, a system for operations an energy plant obtains thermal energy load allocation data indicating time dependent thermal energy load of the energy plant. The system determines, for a time period, an operating state of the energy plant from a plurality of predefined operating states based on the thermal energy load allocation data. The system determines operating parameters of the energy plant according to the determined operating state. The system operates the energy plant according to the determined operating parameters.

Abstract: One implementation of the present disclosure is a controller for a variable refrigerant flow system. The controller includes processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations including identifying zones within a structure, generating zone groupings defining zone groups and specifying which of the zones are grouped together to form each of the zone groups, generating metric of success values corresponding to the zone groupings and indicating a control feasibility of a corresponding zone grouping, selecting a zone grouping based on the metric of success values, and using the selected zone grouping to operate equipment of the variable refrigerant flow system to provide heating or cooling to the zones.

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: An HVAC system for automatically adjusting setpoint boundaries of a space includes building equipment configured to provide heating or cooling to the space to affect an environmental condition of the space and a controller. The controller obtains occupant setpoint adjustment data indicating occupant setpoint increases or occupant setpoint decreases at multiple times during a time interval and partitions the occupant setpoint adjustment data into time period bins based on the multiple times associated with the occupant setpoint adjustment data, each of the time period bins containing occupant setpoint adjustment data characterized by a common time attribute. The controller determines a number of occupant setpoint increases and a number of occupant setpoint decreases indicated by the occupant setpoint adjustment data within each time period bin and adjusts a setpoint boundary of the space based on the number of occupant setpoint increases or the number of occupant setpoint decreases.

Abstract: A building management system for generating a building model for a building and operating building equipment of the building based on the building model. The system includes a processing circuit configured to receive a context, wherein the context includes metadata defining the building model for the building and generate a building model editor interface for viewing and editing the received context, wherein the building model interface includes building elements for the building model, wherein the building elements are based on the received context and represent the building equipment. The processing circuit is configured to receive user edits of the context via the building model interface, wherein the user edits include edits to the building elements, generate an updated context based on the user edits of the context, and deploy the updated context to control environmental conditions of the building with the building equipment based on the updated context.

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: Granular assets of a building are identified. Each granular asset represents one or more devices of that operate to produce, consume, or store resources in the building. General assets are defined by grouping the granular assets into groups. A non-granular optimization is performed to determine a non-granular allocation of the resources among the general assets. Non-granular allocation defines an amount of each of the resources consumed, produced, or stored by each of the general assets at each time step in a time period. A granular optimization is performed for each general asset to determine a granular allocation of the resources among the granular assets. The granular allocation defines an amount of each resource consumed, produced, or stored by the granular assets at each time step in the time period. The equipment of the building are operated to consume, produce, or store the amount of each resource defined by granular allocation.

Abstract: A building management system for generating a building model for a building and operating building equipment of the building based on the building model. The system includes a processing circuit configured to receive a context, wherein the context includes metadata defining the building model for the building and generate a building model editor interface for viewing and editing the received context, wherein the building model interface includes building elements for the building model, wherein the building elements are based on the received context and represent the building equipment. The processing circuit is configured to receive user edits of the context via the building model interface, wherein the user edits include edits to the building elements, generate an updated context based on the user edits of the context, and deploy the updated context to control environmental conditions of the building with the building equipment based on the updated context.

Abstract: A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.

Abstract: A controller for a plurality of heating, ventilation, or air conditioning (HVAC) devices includes a processing circuit that includes one or more processors and memory. The controller detects a change in condition that affects an operating status of a first HVAC device of the plurality of HVAC devices. The controller uses schematic relationships between the plurality of HVAC devices to determine a reduced subset of the plurality of HVAC devices for which operating parameters are to be generated based on the operating status of the first HVAC device. The controller generates operating parameters for the reduced subset of the plurality of HVAC devices and operates the plurality of HVAC devices using the operating parameters.

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 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 control system for a central energy facility with distributed energy storage includes a high level coordinator, a low level airside controller, a central plant controller, and a battery controller. The high level coordinator is configured to perform a high level optimization to generate an airside load profile for an airside system, a subplant load profile for a central plant, and a battery power profile for a battery. The low level airside controller is configured to use the airside load profile to operate airside HVAC equipment of the airside subsystem. The central plant controller is configured to use the subplant load profile to operate central plant equipment of the central plant. The battery controller is configured to use the battery power profile to control an amount of electric energy stored in the battery or discharged from the battery at each of a plurality of time steps in an optimization period.