AIoT system for Smart Office: Device Automation & prediction algorithm

Abstract

Air-conditioning and lighting systems are necessary components in the modern office, generating wastage in idle mode due to our behavior changes like off work or lunch break. In the U.S., nearly 405 TWh (USD 56 Billion) is wasted annually due to device idling. Hence, a Behavior Learning and Energy Saving System(BLESS) with the main control unit and wireless device controllers, including a universal I.R. controller, curtain controller, and lighting controller, is designed to efficiently control office devices to reduce redundant usage and provide enough comfortability for better working performance. An evaluation unit is designed for system performance tracking and a cloud server setup for data logging and algorithm executing purposes. The algorithm will learn the user behavior by human sensors and optimize the control profile and control the devices accordingly. Using the Kalman filter as a filtering algorithm, data from the human sensor can be learned as a weekly profile. Besides, a fuzzy logic algorithm is adopted to control the environment in real-time, and a user interface is designed to enable active control for users by comparing the result with an office that used BLESS and a regular office. The system can save over 47% of wasted energy with a comfortable environment in most operation periods.