A NILM method for cooling load disaggregation based on artificial neural network

Ziwei Xiao; Jiaqi Yuan; Wenjie Gang; Chong Zhang; Xinhua Xu

doi:10.1051/e3sconf/201911105020

All issues

Volume 111 (2019)

E3S Web Conf., 111 (2019) 05020

References

Open Access

Issue		E3S Web Conf. Volume 111, 2019 CLIMA 2019 Congress


Article Number		05020
Number of page(s)		6
Section		Information and Communication Technologies (ICT) for the Intelligent Building Management
DOI		https://doi.org/10.1051/e3sconf/201911105020
Published online		13 August 2019

Zoha A, Gluhak A, Imran MA, Rajasegarar S. Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. Sensors. 2012;12 (12): 16838. [CrossRef] [Google Scholar]
Hart GW. Nonintrusive appliance load monitoring. Proceedings of the IEEE. 1992; 80 (12): 1870–91. [CrossRef] [Google Scholar]
Zeifman M, Roth K. Nonintrusive Appliance Load Monitoring: Review and Outlook. IEEE Transactions on Consumer Electronics. 2011; 57 (1): 76–84. [CrossRef] [Google Scholar]
Advanced metering initiatives and residential feedback programs: a meta-review for household electricity saving opportunities. [Google Scholar]
Basu K, Debusschere V, Bacha S, Hably A, Van Delft D, Jan Dirven G. A generic data driven approach for low sampling load disaggregation. Sustainable Energy, Grids and Networks. 2017; 9: 118–27. [CrossRef] [Google Scholar]
Cominola A, Giuliani M, Piga D, Castelletti A, Rizzoli AE. A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring. Applied Energy. 2017; 185: 331–44. [Google Scholar]
Wang Y, Li X. Unorganized ventilation in subway stations with Platform Screen Doors. Building and Environment. 2017; 125: 556–64. [Google Scholar]
Zhang G, Eddy Patuwo B, Y. Hu M. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting. 1998; 14 (1): 35-62. [Google Scholar]

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[1] Zoha A, Gluhak A, Imran MA, Rajasegarar S. Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. Sensors. 2012;12 (12): 16838. [CrossRef] [Google Scholar]

[2] Hart GW. Nonintrusive appliance load monitoring. Proceedings of the IEEE. 1992; 80 (12): 1870–91. [CrossRef] [Google Scholar]

[3] Zeifman M, Roth K. Nonintrusive Appliance Load Monitoring: Review and Outlook. IEEE Transactions on Consumer Electronics. 2011; 57 (1): 76–84. [CrossRef] [Google Scholar]

[4] Advanced metering initiatives and residential feedback programs: a meta-review for household electricity saving opportunities. [Google Scholar]

[5] Basu K, Debusschere V, Bacha S, Hably A, Van Delft D, Jan Dirven G. A generic data driven approach for low sampling load disaggregation. Sustainable Energy, Grids and Networks. 2017; 9: 118–27. [CrossRef] [Google Scholar]

[6] Cominola A, Giuliani M, Piga D, Castelletti A, Rizzoli AE. A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring. Applied Energy. 2017; 185: 331–44. [Google Scholar]

[7] Wang Y, Li X. Unorganized ventilation in subway stations with Platform Screen Doors. Building and Environment. 2017; 125: 556–64. [Google Scholar]

[8] Zhang G, Eddy Patuwo B, Y. Hu M. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting. 1998; 14 (1): 35-62. [Google Scholar]