Abstract
Systems and methods for health and energy co-optimization in building monitoring are disclosed herein, including methods and systems for monitoring a building and automatically identifying opportunities to realize energy savings without comprising the health, comfort, and productivity of building occupants. According to at least one aspect of the disclosure, machine learning methods may be applied to sensor data indicating one or more parameters related to indoor air quality (IAQ) of a building (such as carbon dioxide data and indoor air temperature data) to determine an estimate of the timing of building occupancy and an estimate of the timing of building operations. Based on these estimates, various scenarios may be identified, including time periods representing incidents of extended operations or extended occupancy. In various implementations, recommendations may be generated to adjust building systems based on the identified incidents and IAQ performance indicators for the time period. For example, such recommendations may be to change a mode of building operation at a particular time.