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

Abstract 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.
- City University of Hong Kong China (People's Republic of)
- Durham University United Kingdom
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).65 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
