Supervised training data generation for interior environment simulation

Abstract

A dense array of sensors positioned in a virtual environment is reduced to a sparse array of sensors in a physical environment, which provides sufficient information to a controller that responds to environmental conditions and parameters in the physical environment in substantially the same manner as it would to the same environmental conditions and parameters in the equivalent virtual environment. Data from a sparse array of virtual sensors is correlated with data from a dense array of virtual sensors and is used for generating control signals for hardware devices that influence a real or virtual interior environment. The correlated data and the control signals are used to train an artificial intelligence based controller that then controls the values of the parameters of the interior environment. A model of the interior environment is created using basic parameters in a computer-aided design application.