Abstract: A method includes providing, by processing circuitry, a net resource consumption trajectory including net resource consumption targets for one or more subperiods of a time period. Each net resource consumption target indicates a target difference between resource consumption and resource production or offset for a subperiod of the one or more subperiods. The method includes generating, by the processing circuitry and for a subperiod of the one or more subperiods, a set of actions predicted to achieve the net resource consumption target for the subperiod. The method includes implementing, by the processing circuitry, the set of actions.

Abstract: A method includes providing a net consumption trajectory comprising net consumption targets for one or more subperiods of a time period. Each net consumption target indicates a target difference from a beginning of a time period to an end of the subperiod between total consumption and total production or offset. The method also includes generating, for a subperiod of the plurality of subperiods, a set of curtailment actions predicted to achieve the net consumption target for the subperiod and implementing the set of curtailment actions.

Abstract: A thermal optimization control includes processing circuitry. The processing circuitry is operable to receive information from a building housing at least one computer server, with the information including at least current thermal load. The processing circuitry is operable to control an associated HVAC system for at least an area of the building where the at least one computer server is housed. The processing circuity is operable to optimize the operation of the HVAC system based upon the current and an expected upcoming thermal load on the area of the building where the server is received to reduce energy usage by the HVAC system. A method and building are also disclosed.

Abstract: A thermostat for a building zone includes at least one of a model predictive controller and an equipment controller. The model predictive controller is configured to obtain a cost function that accounts for a cost of operating HVAC equipment during each of a plurality of time steps, use a predictive model to predict a temperature of the building zone during each of the plurality of time steps, and generate temperature setpoints for the building zone for each of the plurality of time steps by optimizing the cost function subject to a constraint on the predicted temperature. The equipment controller is configured to receive the temperature setpoints generated by the model predictive controller and drive the temperature of the building zone toward the temperature setpoints during each of the plurality of time steps by operating the HVAC equipment to provide heating or cooling to the building zone.

Abstract: A model predictive maintenance (MPM) system for building equipment includes one or more processing circuits having one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including estimating a degradation state of the building equipment, using a degradation impact model to predict an amount of one or more input resources consumed by the building equipment to produce one or more output resources based on the degradation state of the building equipment, generating a maintenance schedule for the building equipment based on the amount of the one or more input resources predicted by the degradation impact model, and initiating a maintenance activity for the building equipment in accordance with the maintenance schedule.

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: A controller for HVAC equipment uses a predictive model for the HVAC equipment to calculate a plurality of values of a model-predicted heating or cooling load of the HVAC equipment at a plurality of time steps within a time period, obtains a plurality of values of an actual heating or cooling load of the HVAC equipment at the plurality of time steps within the time period, generates a calibration model that relates the model-predicted heating or cooling load to the actual heating or cooling load using the plurality of values of the model-predicted heating or cooling load and the plurality of values of the actual heating or cooling load, uses the calibration model to calculate calibrated values of the model-predicted heating or cooling load of the HVAC equipment, and operates the HVAC equipment using the calibrated values of the model-predicted heating or cooling load.

Abstract: Systems and methods for reducing health risks with respect to an infectious disease in buildings. Health data for an infectious diseases is used to determine a health risk level for building spaces and individuals in the building. An air handling action or a disinfection action is performed based on the health risk level.

Abstract: A system includes a first subsystem configured to produce a resource by consuming electricity, a second subsystem configured to produce the resource by consuming a fuel, and a controller. The controller is configured to determine an allocation of a predicted demand for the resource over a future time period between the first subsystem and the second subsystem based on a first carbon emissions rate associated with off-site production of the electricity and a second carbon emissions rate associated with on-site consumption of the fuel. The controller is also configured to control the first subsystem and the second subsystem to produce the resource in accordance with the allocation during the future time period.

Abstract: A building system that operates to receive a plurality of data samples from building equipment of the building and identity whether one or more data samples of the plurality of data samples are anomalous indicating that one or more pieces of the building equipment are possibly experiencing a fault. The building system operates to generate one or more data health scores for the building equipment based on whether the one or more data samples are anomalous, the one or more data health scores indicating quality levels of the plurality of data samples and generate user interface data configured to cause a user device to display a user interface providing indications of the one or more pieces of building equipment that are possibly experiencing the fault and the one or more data health scores.

Abstract: A model predictive maintenance (MPM) system for building equipment 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 obtaining one or more performance indicators for the building equipment and determining whether a trigger condition has been satisfied based on the one or more performance indicators. The operations include triggering a model predictive maintenance process to generate a maintenance schedule for the building equipment in response to determining that the trigger condition has been satisfied. The operations include initiating a maintenance activity for the building equipment in accordance with the maintenance schedule.

Abstract: A thermostat for a building zone includes at least one of a model predictive controller and an equipment controller. The model predictive controller is configured to obtain a cost function that accounts for a cost of operating HVAC equipment during each of a plurality of time steps, use a predictive model to predict a temperature of the building zone during each of the plurality of time steps, and generate temperature setpoints for the building zone for each of the plurality of time steps by optimizing the cost function subject to a constraint on the predicted temperature. The equipment controller is configured to receive the temperature setpoints generated by the model predictive controller and drive the temperature of the building zone toward the temperature setpoints during each of the plurality of time steps by operating the HVAC equipment to provide heating or cooling to the building zone.

Abstract: A method includes finding, in equipment performance data points defined by values of an independent variable and values of a dependent variable, a first point for which a convexity metric of the dependent variable with respect to the independent variable satisfies a convexity criterion, creating an equipment curve by providing a first convex region on a first side of the first point and a second convex region on a second side of the first point, and controlling the building equipment based on the equipment curve.

Abstract: A method includes performing a plurality of assessments of different characteristics of data for a segment of the training period, including the data for the segment in a set of training data or excluding the data from the set of training data based on results of the plurality of assessments for the segment, repeating the plurality of assessments for additional data of a plurality of additional segments of the training period, including the additional data in the set of training data or excluding the additional data from the set of training data based on results of the plurality of assessments for the plurality of additional segments, training a system model for a system using the set of training data, controlling the system using the system model.

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 predictive controller for building equipment associated with a building includes one or more processing circuits configured to control electric energy used by the building equipment. The building equipment includes an electric energy using component. The one or more processing circuits are configured to utilize a predictive cost function to determine a first amount of the electric energy supplied from an energy grid source and a second amount of the electric energy supplied from a second energy source to the electric energy using component.

Abstract: A controller for operating building equipment of a building including processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including obtaining a first setpoint trajectory from a cloud computation system. The first setpoint trajectory includes setpoints for the building equipment or for a space of the building. The setpoints correspond to time steps of an optimization period. The operations include determining whether a connection between the controller and the cloud computation system is active or inactive at a time step of the optimization period and determining an active setpoint for the time step of the optimization period using either the first or second setpoint trajectory based on whether the connection between the controller and the cloud computation system is active or inactive at the time step. The operations include operating the building equipment based on the active setpoint.

Abstract: A building system operates to receive building data from one or more building data sources associated with the building, wherein at least a portion of the building data is associated with control of the one or more environmental conditions of the building and generate, based on the building data, one or more data quality indicators for the building data received from the one or more building data sources, the one or more data quality indicators indicating quality levels of the building data. The building system operates to generate user interface data configured to cause a user device to display a user interface providing indications of the one or more data quality indicators.

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 method includes finding, in subplant performance data points defined by values of an independent variable and values of a dependent variable, a first point for which a convexity metric of the dependent variable with respect to the independent variable exceeds a threshold, creating a subplant curve by providing a first convex region on a first side of the first point and a second convex region on a second side of the first point, and controlling the campus subplant based on the subplant curve.