Abstract: An control method includes obtaining measurements of a carbon dioxide (CO2) level of a zone and a temperature of the zone. The method includes determining, based on the CO2 level of the zone and the temperature of the zone, a combination if (i) control decisions for an air handling unit (AHU) and (ii) control decisions for a damper of a variable air volume (VAV) unit to satisfy both a ventilation control objective and a temperature control objective. The control decisions for the AHU include adjustments to a fresh air intake fraction of the AHU between multiple values over time, and the control decisions for the VAV unit include adjustments to a position of a damper of the VAV unit over time. The method includes operating the AHU according to the control decisions for the AHU and operate the VAV unit according to the control decisions for the VAV unit.

Abstract: A method includes obtaining an optimized setpoint schedule for a time period, identifying a pre-cooling or pre-heating segment of the time period of the optimized setpoint schedule, adjusting the optimized setpoint schedule based on a characteristic of the pre-cooling or pre-heating segment to obtain an adjusted setpoint schedule, and operating the building equipment in accordance with the adjusted setpoint schedule.

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: 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 at least one of a level of the one or more characteristics of the first fluid or the one or more characteristics of the second fluid.

Abstract: A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

Abstract: An energy storage system for a building includes a battery asset configured to store electricity and discharge the stored electricity for use in satisfying a building electric load. The system includes a planning tool configured to identify one or more selected functionalities of the energy storage system and generate a cost function defining a cost of operating the energy storage system over an optimization period. The cost function includes a term for each of the selected functionalities. The planning tool is configured to generate optimization constraints based on the selected functionalities, attributes of the battery asset, and the electric energy load to be satisfied. The planning tool is configured to optimize the cost function to determine optimal power setpoints for the battery asset at each of a plurality of time steps of the optimization period.

Abstract: A model predictive maintenance (MPM) system for building equipment. The MPM system includes an equipment controller configured to operate the building equipment to affect a variable state or condition in a building. The MPM system includes an operational cost predictor configured to predict a cost of operating the building equipment over a duration of an optimization period. The MPM system includes a budget manager configured to generate one or more budget constraints. The MPM system includes an objective function optimizer configured to optimize an objective function subject to the one or more budget constraints to determine a maintenance and replacement schedule for the building equipment. The objective function includes maintenance and replacement costs of the building equipment and the predicted cost of operating the building equipment.

Abstract: Systems and methods for automatically commissioning and operating a heating, ventilation, or air conditioning (HVAC) system for a building site are provided. An exemplary method includes constructing a model using physical equipment of the HVAC system and relationships between the physical equipment. The model indicates connections between the physical equipment and one or more resources produced or consumed by the physical equipment. The method includes generating a mapping between points of the physical equipment at the building site and corresponding variables of the model, using the model to generate values of one or more control variables of the model, and operating the physical equipment by providing the values of the control variables to corresponding points of the physical equipment based on the mapping.

Abstract: A control system for cost optimal operation of an energy facility including equipment includes a controller configured to provide a cost function comprising a cost term defining a cost as a function of a rate variable and an equipment usage variable, simulate the cost of operating the energy facility over an optimization period at each of a plurality of different values of the rate variable which define a plurality of different costs per unit of the equipment usage variable, select a value of the rate variable that results in a lowest cost of operating the energy facility over the optimization period, perform an online optimization of the cost function with the rate variable set to the selected value to generate one or more setpoints for the equipment, and operate the equipment during the optimization period in accordance with the generated setpoints.

Abstract: A model predictive maintenance system for building equipment. The system includes one or more processing circuits including 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. The operations include performing an optimization of an objective function that defines a present value of a total cost of operating the building equipment and performing maintenance on the building equipment as a function of operating decisions and maintenance decisions for the building equipment for time steps within a time period. The total cost includes one or more costs incurred during one or more future time steps of the time period. The operations include operating the building equipment and performing maintenance on the building equipment in accordance with decisions defined by the result of the optimization.

Abstract: A system for assessing relative performance among a plurality of heating, ventilation, or air condition (HVAC) assets, the system comprising a processing circuit configured to identify a peer group comprising two or more of the plurality of HVAC assets having a common characteristic, generate values of a peer metric for the HVAC assets in the peer group based on corresponding operation data associated with the HVAC assets, perform a weighted outlier detection process using the values of the peer metric, and initiate an automated action in response to detecting an outlier HVAC asset.

Abstract: Systems and methods for detecting and correcting faults in direct evaporative cooling units are provided. In an exemplary embodiment, direct evaporative cooling affects a humidity and a temperature of the data center. An exemplary method includes generating expected values for the humidity of the data center and expected values for the temperature of the data center using a model. A fault condition is determined in response to the actual values for the humidity deviating from the expected values for the humidity while actual values for the temperature track the expected values for the temperature, or in response to the actual values for the temperature deviating from the expected values for the temperature while actual values for the humidity track the expected values for the humidity.

Abstract: A method of controlling a direct evaporative cooling unit includes predicting water consumption and energy consumption of the direct evaporative cooling unit based on possible supply air temperatures of the direct evaporative cooling unit, selecting a target supply air temperature based on an optimization of an objective function, the objective function comprising the water consumption and energy consumption, and controlling the direct evaporative cooling unit in accordance with the target supply air temperature.

Abstract: A method includes providing a control algorithm that may include a constraint constraining production of the equipment below maximum values for a plurality of time steps, and dynamically adjusting the constraint by updating the maximum values as a function of predicted values of a dynamic variable for the plurality of time step. The dynamic variable affects an actual maximum production of the equipment. The method includes determining control decisions for the plurality of time steps by executing the control algorithm and controlling the equipment in accordance with the control decisions.

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 heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.

Abstract: An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

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 controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

Abstract: A controller for equipment that operates to affect a variable state or condition of a building including one or more processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include generating a new predictive model using training data associated with one or more durations selected from a time period and selected to satisfy a set of criteria. The predictive model models system dynamics of the building during the time period. The operations include storing the new predictive model in a database including predictive models that model the system dynamics of the building and include comparing performance of the new predictive model and the predictive models stored by the database to select a particular predictive model for controlling the equipment. The operations include using the particular predictive model to generate and provide control signals to the equipment.