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2024 | OriginalPaper | Buchkapitel

15. Building Occupancy and Smart Metering

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Abstract

Buildings have been recognized as one of the major carbon emission sources to maintain the basic requirement of building occupants, and occupant has been widely recognized as key building energy end-users. Therefore, to improve building energy efficiency, sensing, recognizing, and utilizing occupancy patterns for building demand-based control are very important and efficient ways. This chapter presents those contents: (i) Occupancy resolution for building energy-savings and its relationship between circular economy at building sector; (ii) Current occupancy sensing and related energy-efficient approach; (iii) Occupancy smart sensing with Wi-Fi network; (iv) One case study of how occupancy sensing can improve building energy efficiency.

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Literatur
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Zurück zum Zitat Davis, J. A., & Nutter, D. W. (2010). Occupancy diversity factors for common university building types. Energy and Buildings, 42, 1543–1551.CrossRef Davis, J. A., & Nutter, D. W. (2010). Occupancy diversity factors for common university building types. Energy and Buildings, 42, 1543–1551.CrossRef
Zurück zum Zitat Ekwevigbe, T., Brown, N., Pakka, V. H., & Fan, D. (2013). Real-time building occupancy sensing for supporting demand driven HVAC operations. International Conference for Enhanced Building Operations. Ekwevigbe, T., Brown, N., Pakka, V. H., & Fan, D. (2013). Real-time building occupancy sensing for supporting demand driven HVAC operations. International Conference for Enhanced Building Operations.
Zurück zum Zitat Korkas, C. D., Baldi, S., Michailidis, I., & Kosmatopoulos, E. B. (2016). Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage. Applied Energy, 163, 93–104.CrossRef Korkas, C. D., Baldi, S., Michailidis, I., & Kosmatopoulos, E. B. (2016). Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage. Applied Energy, 163, 93–104.CrossRef
Zurück zum Zitat Peschiera, G., Taylor, J. E., & Siegel, J. A. (2010). Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data. Energy and Buildings, 42, 1329–1336.CrossRef Peschiera, G., Taylor, J. E., & Siegel, J. A. (2010). Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data. Energy and Buildings, 42, 1329–1336.CrossRef
Zurück zum Zitat Sidiropoulos, N., Mioduszewski, M., & Oljasz, P., & Schaap Edwin Schaap, E. (2012). Open Wifi SSID Broadcast vulnerability SSN Project Assessment 2012. Sidiropoulos, N., Mioduszewski, M., & Oljasz, P., & Schaap Edwin Schaap, E. (2012). Open Wifi SSID Broadcast vulnerability SSN Project Assessment 2012.
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Zurück zum Zitat Wang, W., Lin, Z., & Chen, J. (2017d). Promoting energy efficiency of HVAC operation in large office spaces with a Wi-Fi probe enabled Markov time window occupancy detection approach. In World Engineers Summit – Applied Energy Symposium Forum Low Carbon Cities Urban Energy Joint Conference WES-CUE 2017. Singapore. Wang, W., Lin, Z., & Chen, J. (2017d). Promoting energy efficiency of HVAC operation in large office spaces with a Wi-Fi probe enabled Markov time window occupancy detection approach. In World Engineers Summit – Applied Energy Symposium Forum Low Carbon Cities Urban Energy Joint Conference WES-CUE 2017. Singapore.
Metadaten
Titel
Building Occupancy and Smart Metering
verfasst von
Wei Wang
Copyright-Jahr
2024
DOI
https://doi.org/10.1007/978-3-031-56241-9_15