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NTIS 바로가기Journal of the Institute of Electronics and Information Engineers = 전자공학회논문지, v.55 no.7 = no.488, 2018년, pp.31 - 39
최준혁 (인천대학교 컴퓨터공학부) , 김지범 (인천대학교 컴퓨터공학부)
In this paper, various machine learning and deep learning algorithms are applied to analyze hourly water consumption data collected from real smart water meter systems. In particular, we have studied three methods where water suppliers can reduce non-revenue water in smart water meter systems. First...
A. Candelieri, D. Soldi and F. Archetti, "Short-term forecasting of hourly water consumption by using automatic metering readers data," in Proc. of Computer Control for Water Industry Conference, pp. 843-853, Leicester, UK, September, 2015.
B. Kingdom, R. Liemberger, and P. Marin, "The challenge of reducing non-revenue (NRW) water in developing countries. How the private sector can help: A look at performance-based service contracting," In Water Supply and Sanitation Sector Board Discussion Paper Series, no. 8, 2006.
D. Marino, K. Amarasinghe, and M. Manic, "Building energy load forecasting using deep neural networks," in Proc. of Annu. Conf. IEEE Industrial Electronics Society, Florence, Italy, Oct. 2016.
E. Kermany, H. Mazzawi, D. Baras, Y. Naveh, and H. Michaelis. "Analysis of advanced meter infrastructure data of water consumption in apartment buildings," in Proc. of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013.
V. Gulisano, M. Almgren, and M. Papatriantafilou, "Online and scalable data validation in advanced metering infrastructures," in Proc. of the 5th IEEE PES Innov. Smart Grid Technol. Eur. Conf., pp. 1-6, Istanbul, Turkey, Oct., 2014.
A. Hampapur, H. Cao, A. Davenport, W. S. Dong, D. Fenhagen, R. S. Feris, G. Goldszmidt, Z. B. Jiang, J. Kalagnanam, T. Kumar, H. Li, X. Liu, S. Mahatma, S. Pankanti, D. Pelleg, W. Sun, M. Taylor, C. H. Tian, S. Wasserkrug, L. Xie, M. Lodhi, C. Kiely, K. Butturff, and L. Desjardins, "Analytics-driven asset management," IBM Journal of Research and Development, 55(1.2):13:1-13:19, 2011.
S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, pp. 1735-1780, 1997.
M. Hubert and M. Debruyne, Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, no. 36, 2010.
B. Hoyle, M.M. Raul, K. Paech, C. Bonnett, S. Seitzl and J. Weller, "Anomaly Detection for Machine Learning Redshifts Applied to SDSS Galaxies," MNRAS, pp. 305-316, Vol. 450, 2015.
D. Lee, S. Kang, J. Shin, "Using deep learning techniques to forecast environmental consumption level" Sustainability, Vol. 9, no. 1894, 2017.
Pedregosa et al., "Scikit-learn: Machine Learning in Python," JMLR, pp. 2825-2830, Vol. 12. 2011.
M. Badi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, et al., "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," arXiv preprint arXiv:1603.04467, 2016.
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