A method for managing air quality may include, at one or more processors, receiving sensor data comprising a plurality of air quality parameters for an environment, wherein the sensor data is generated by one or more environment quality monitoring devices located in the environment, predicting an adverse air quality event based on the sensor data, and automatically controlling one or more devices to mitigate the adverse air quality event. An environment quality monitoring device may include a housing, a plurality of sensors in the housing and configured to generate sensor data comprising a plurality of environment quality parameters, a network communication device configured to communicate the sensor data over a network, and an alert configured to indicate an environment quality score of the ambient environment, where the environment quality score is based on at least a portion of the sensor data.

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.

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.

An environmental control system for a building including heating, ventilation, or air conditioning (HVAC) equipment that operates to affect a temperature of a zone of the building. The system includes a temperature sensor to measure the temperature and a controller including a processing circuit. The processing circuit is configured to operate the HVAC equipment based on a temperature setpoint and gather training data indicating system dynamics. The processing circuit is configured to monitor a temperature tracking error of the zone and a heat transfer value of the HVAC equipment and determine if the HVAC equipment is in a saturation region based on the temperature tracking error and the heat transfer value. The processing circuit is configured to, in response to a determination that the HVAC equipment is in the saturation region, calculate an adjusted temperature setpoint and operate the HVAC equipment based on the adjusted temperature setpoint.

Systems and methods are configured to control operation of an HVAC system providing climate control for a zone of a structure. In various embodiments, a constrained optimization problem is performed to set control commands for controlling operation of the HVAC system to achieve one or more objectives while providing a supply air flow at an air temperature and a humidity ratio for the zone at a future time. For instance, the optimization problem may include a cost function having constraints based on a desired temperature setpoint and a humidity ratio for the zone. A value is set for each control command based on the performance of the constrained optimization problem to achieve at least one of the objectives and as a result, the supply air flow at the air temperature and humidity ratio is provided by the HVAC system at the future time to the zone based on the values.

A technique is described herein for using computing technology to intelligently manage the consumption of a resource in a physical environment and/or controlling the physical environment in other ways. The technique maintains environment information that describes entities within the physical environment, together with the relationships among the entities. The technique leverages the environment information and collected sensor data to generate forecast data using one or more machine-trained models. The technique then leverages the environment information, sensor data, and forecast data to generate a control plan. The control plan provides a strategy for controlling the physical environment that satisfies a specified optimization objective. In one use case, the technique contributes to the efficient consumption of power provided by a distribution system by avoiding consumption of power in periods in which the distribution system is expected to experience high loads.

A control device executes a step of starting a computation processing of a prediction model; a step of computing a remaining processing time until the computation processing is completed after starting the computation processing of the prediction model; a step of determining whether the determination of the command value based on an output obtained from the prediction model is made within a control timing for controlling the operation of manufacturing by the manufacturing device, on the basis of a computed remaining processing time; and a step of stopping, when it is determined that the determination of the command value is not made within the control timing, the computation processing of the prediction model, determining the command value on the basis of a value of an intermediate result of the computation processing, and controlling the operation of the manufacturing device on the basis of the determined command value.

A method for controlling an industrial process includes: determining, by a process controller, based at least in part on a set of current values and/or past values of state variables of the industrial process, a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause a performing of at least one physical action on the process; querying, based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model configured to output a classification value, and/or a regression value, that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller; and determining that the classification value, the regression value, and/or the propensity, meets a predetermined criterion.

The disclosure relates to a sensor network, machine type communication (MTC), machine-to-machine (M2M) communication, and technology for internet of things (IoT). A method of a server in an air conditioning system and a server are provided. The method includes detecting a schedule duration for a compound control zone in which a compound control operation for controlling a ventilating operation and a cooling/heating operation will be performed; determining a ventilating schedule based on an air state change; and determining a cooling/heating schedule.

Embodiments control and optimize batch processes. An embodiment obtains and standardizes historical operating data from a plurality of batch production runs of an industrial process. For each batch production run, the standardized operating data corresponding to the batch is partitioned into one or more stages and one or more signature for each stage is determined using the partitioned standardized data. Each determined signature is associated with a class label based upon whether output of a batch run corresponding to the signature conforms or does not conform with operational standards. A model is trained, with at least a subset of the signatures as inputs and associated class labels as outputs, to predict, based on operating data from a real-world batch process, whether output of the process will conform or not conform with the operational standards. Online predictions can be automatically or manually applied to control and optimize a batch production run.