Abstract: Various embodiments relate to a method, apparatus, and machine-readable storage medium including one or more of the following: identifying a chunk of computer code from a larger process to be executed as a distributed computation; creating a job request specifying the chunk of computer code and data on which the chunk of computer code is to operate; selecting a device from a plurality of devices to process the job request; transmitting the job request to the selected device; receiving a job result from the selected device; continuing the larger process based on the job result.

Abstract: A structure thermodynamic model, which models the physical characteristics of a controlled space, inputs a constraint state curve which gives constraints, such as temperature, that a controlled space is to meet; and outputs a state injection time series which is the amount of state needed for the controlled space to optimize the constraint state curve. The state injection time series curve is then used as input into an equipment model, which models equipment behavior in the controlled space. The equipment model outputs equipment control actions per control time (a control sequence) which can be used to control the equipment in the controlled space. Some embodiments train the models using training data.

Abstract: Various embodiments relate to a method, apparatus, and machine-readable storage medium for using a digital twin including one or more of the following: accessing a digital twin stored in memory, wherein the digital twin includes: a plurality of nodes, each node having at least one normalized interface associated with data having a respective data type, and a plurality of edges connecting respective normalized interfaces associated with like data types among the plurality of nodes; propagating data having a first data type through the plurality of nodes along edges of the plurality of edges connected to normalized interfaces associated with the first data type; and extracting data from at least one node of the plurality of nodes to be used as simulation output.

Abstract: Various embodiments relate to a method, apparatus, and machine-readable storage medium including one or more of the following: creating a reference into the digital twin that accesses a structural property stored in the digital twin; identifying a constraint for the physical structure associated with the reference; creating a cost function from the reference and the constraint; and optimizing a value for structural property using the cost function, whereby the structural property stored in the digital twin is corrected.

Abstract: Tools and techniques are described to automate line testing when wiring devices (such as equipment and sensors) to controllers. Controllers have access to databases of the devices that are controlled by them, including wiring diagrams and protocols, such that the controller can automatically check that each wire responds correctly to stimulus from the controller. After testing, a reporting device rapidly shows the results of the line testing.

Abstract: Various embodiments described herein relate to a method, device, and non-transitory machine-readable storage medium for controlling a system having a plurality of devices including one or more of the following: calculating a demand curve for a controlled space representing at least one target amount of state for delivery to the controlled space; creating a cost function that compares the demand curve to simulated state curves produced by a model of the controlled system based on respective sets of control actions issuable to the plurality of controlled devices; performing an optimization method that tunes a candidate set of control actions to reduce a cost output by the cost function based on the candidate set of control actions; and issuing control actions of the candidate set of control actions to respective ones of the plurality of controlled devices to cause the controlled system to deliver state to the controlled space.

Abstract: A model receives a target demand curve as an input and outputs an optimized control sequence that allows equipment within a physical space to be run optimally. A thermodynamic model is created that represents equipment within the physical space, with the equipment being laid out as nodes within the model according to the equipment flow in the physical space. The equipment activation functions comprise equations that mimic equipment operation. Values flow between the nodes similarly to how states flow between the actual equipment. The model is run such that a control sequence is used as input into the neural network; the neural network outputs a demand curve which is then checked against the target demand curve. Machine learning methods are then used to determine a new control sequence. The model is run until a goal state is reached.

Abstract: Various embodiments disclosed herein relate to a building automation controller and related method and storage medium including a processor configured, through at least execution of a distributed computer program, to: receive sensor data generated by a sensor, wherein the sensor data is indicative of a state of a defined space, identify an action to be performed by a device to affect the state in accordance with an operating characteristic for the defined space, determine that the device is attached to a second controller of a plurality of additional controllers, and transmit to the second controller, an indication that the action is to be performed by the device, wherein: the distributed computer program is configured to be distributed among the processor and the plurality of additional controllers and, the processor is further configured to apportion work to be performed by the computer program between at least the additional controllers.

Abstract: Tools and techniques are described to modify a defined space state depending on number of users in the space and/or preferences of users in the space. In some embodiments, users entering or leaving a space are noticed by network systems. A controller then modifies resources in the space to account for the greater or lesser load. In other cases, the network system notices that a specific user has entered a building. This user may have preferences stored in the system which the building control system then responds to by changing state of a device that controls physical state within the space.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for organizing automatic control in automation systems from a system description, using deconstruction of complex equipment graphs. A system control scheme is automatically generated from a deconstruction of an equipment graph into controllable sets of prioritized sub-systems. An equipment graph comprises one or more subsystems of equipment. Prioritized sub-systems comprise a unique routing path through an equipment graph. Prioritized sub-systems comprise the ability to be actuated and are divided into groups of sub-system sets. Groups of sub-system sets comprise synchronous and asynchronous sets and are created for conjoined routing paths of parallel sub-systems.

Abstract: Apparatuses, systems, and methods of physical-model based building automation using in-situ regression to optimize control systems are presented. A simulation engine is configured to simulate a behavior or a controlled system using a physical model for the controlled system. A data stream comprises data from a controlled system. A training loop is configured to compare an output of a simulation engine to a data stream using a heuristic so that a physical model is regressed in a manner that the output of the simulation engine approaches the data stream.

Abstract: Tools and techniques are described to attach a device to a controller, whereby the controller analyzes the device inputs, looks up information about the device in a database, and then determines which inputs on the device match the defined device inputs. It then may translate information received from the device into an intermediate language. It may also use the information received from the device, the location of the device, and information about the device to create a digital twin of the device.