公开(公告)号：US20190338974A1

公开(公告)日：2019-11-07

申请号：US16405724

申请日：2019-05-07

Applicant: Johnson Controls Technology Company

Inventor： Robert D. Turney ,  Mohammad N. ElBsat ,  Matthew J. Ellis ,  Anas W. I. Alanqar ,  Michael J. Wenzel

IPC: F24F11/47 ,  F24F11/56 ,  F24F11/64 ,  F24F11/65 ,  F24F11/70 ,  G05B19/042

Abstract: A controller for maintaining occupant comfort in a space of a building. The controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include obtaining building data and obtaining occupant comfort data. The operations include generating an occupant comfort model relating the building data to a level of occupant comfort within the space based on the building data and the occupant comfort data. The operations include generating time-varying comfort constraint for an environmental condition of the space using the occupant comfort model and include performing a cost optimization of a cost function of operating building equipment over a time duration to determine a setpoint for the building equipment. The operations include operating the building equipment based on the setpoint to affect the variable state or condition of the space.

公开(公告)号：US20190316802A1

公开(公告)日：2019-10-17

申请号：US15953324

申请日：2018-04-13

Applicant: Johnson Controls Technology Company

Inventor： Anas W. I. Alanqar ,  Matthew J. Ellis ,  Michael J. Wenzel ,  Juan Esteban Tapiero Bernal

IPC: F24F11/64 ,  G05B13/04 ,  G05B13/02

Abstract: A building management system includes a controller configured to control building equipment by providing a control input to the building equipment for each of the plurality of time steps and generate a set of training data for a system model for the building. The training data includes input training data and output training data for each of the plurality of time steps. The controller is further configured to perform a system identification process to identify parameters of the system model. The system identification process includes predicting, for each time step, a predicted value for one or more of the output variables for each of a plurality of subsequent time steps, generating a prediction error function by comparing the output training data to the predicted values, and optimizing the prediction error function to determine values for the parameters of the system model that minimize the prediction error function.