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 described herein relate to a test chamber device an associated methods and non-transitory machine-readable media including a test chamber, a system builder that is configured to build and apply an equipment load to the test chamber; a load maker that is configured to build and apply a predefined load to the test chamber; and a tester which measures action of the equipment load and the predefined load within the test chamber, producing a test state.

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: 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: After installation, a device may be asleep. A light signal device may send a message to the sleeping device to wake it up. This wake-up message may comprise the light signal device sending programmed light signals, the programmed light signals in modified morse code. An authentication part may also be included in the message. The light signal device may request an authentication message from the sleeping device.

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: 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.