Assessing occupants’ energy load variation through existing wireless network infrastructure in commercial and educational buildings

• Published in: Energy and buildings Vol. 82; pp. 540 - 549
• Main Authors: Chen, Jiayu, Ahn, Changbum
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01.10.2014; Elsevier
• Subjects: Applied sciences; Building technical equipments; Buildings; Buildings. Public works; Commercial and educational buildings; Commercial building; Commercial buildings; Disengaging; Education; Education and research facilities; Energy efficiency; Energy load variation; Energy management and energy conservation in building; Energy use; Environmental engineering; Exact sciences and technology; Feedback; Joints; Types of buildings; Wi-Fi network; Wireless networks; United States; North America; America; Wi-Fi network; Energy efficiency; Energy load variation; Feedback; Commercial and educational buildings; Energy consumption; Energy charge; Feedback regulation; Experimental study; Communication network; Commercial facility; Design; Logistic regression; Energetic efficiency; System description; Educational facility; Energy management; Computing method
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2014.07.053

•We examine the validity of Wi-Fi events as an indicator of energy load changes.•The Wi-Fi connection events have a positive relationship with energy load increase.•The number of Wi-Fi connections has no direct correlation to building energy load. Providing energy-consumption feedback has proven to be an effective approach for changing people's behavior and has led to significant energy-consumption reductions in residential buildings. However, providing feedback in commercial and educational buildings is challenging because of the difficulty in tracking occupants’ behaviors and their corresponding energy usage – especially for temporary occupants. To make providing such feedback possible in commercial and educational buildings, this paper presents the framework for a coupled system that uses residents’ wireless devices’ Wi-Fi connection and disconnection events to detect occupancy and then benchmarks energy loads against these events to monitor the energy use of occupants. A preliminary experiment implemented the proposed approach in a small-scale educational building to ascertain whether Wi-Fi network connection/disconnection events can be an effective indicator of energy load variation. The experiment's results confirmed the positive relationship between the Wi-Fi connection events and energy load increase; these results also indicated that the number of Wi-Fi connections cannot directly represent the magnitude of the energy load. A validation test was also conducted to assess the robustness of the coupled system in terms of the impact of users’ schedules (AM/PM), their length of stay (long-term/temporary), and the locations of access points.