Extracting occupants' energy-use patterns from Wi-Fi networks in office buildings

Wi-Fi networks are currently considered as an efficient and economical tool for occupancy sensing in office buildings. Studies particularly indicated that these networks could be utilized to understand/predict occupants' energy-use patterns. Despite the value that investigating this possibility could provide for the current research, it has not been well explored how energy-use pattern information could be extracted from Wi-Fi system information. In response, this study utilizes statistical analyses to investigate the correlation of Wi-Fi flows with miscellaneous electric loads (MELs) in office buildings. MELs account for more than one-third of office-building energy consumption and are the best representative of occupants' energy-use patterns. In the pursuit of the objective, data from two offices were collected over a 3-month period of time. Results from the analyses show that an average 92% of MELs energy consumption could be predicted through the Wi-Fi flows in a building. This finding thereby demonstrates that occupants' energy-use patterns are highly positively correlated to Wi-Fi flows in a building and accordingly, the information of Wi-Fi networks could be utilized to understand/interpret these patterns. This significantly contributes to the current body of research and can be used to support efforts into understanding/enhancing occupants' energy-use behaviors. In addition, since Wi-Fi networks are a major subset of internet of things (IoT) hardware systems and IoT implementation for intelligent energy management in buildings significantly depends on occupant energy-use patterns, this research helps IoT-based efforts by displaying how these patterns could be extracted from IoT infrastructure.