TY - JOUR
T1 - Pool detection from smart metering data with convolutional neural networks
AU - Ferner, C.
AU - Eibl, G.
AU - Unterweger, A.
AU - Burkhart, S.
AU - Wegenkittl, S.
N1 - Cited By :2
Export Date: 14 December 2023
Correspondence Address: Ferner, C.; Salzburg University of Applied Sciences, Urstein Sued 1, Austria; email: [email protected]
Funding details: Bundesministerium für Verkehr, Innovation und Technologie, BMVIT
Funding details: Salzburger Landesregierung
Funding text 1: The financial support by the Federal State of Salzburg is gratefully acknowledged. Publication of this supplement was funded by Austrian Federal Ministry for Transport, Innovation and Technology.
References: Asghar, M.R., Dán, G., Miorandi, D., Chlamtac, I., Smart meter data privacy: A survey (2017) IEEE Commun Surv Tutor, 19 (4), pp. 2820-2835; Azarova, V., Engel, D., Ferner, C., Kollmann, A., Reichl, J., Exploring the impact of network tariffs on household electricity expenditures using load profiles and socio-economic characteristics (2018) Nat Energy, 3, pp. 317-325; Beckel, C., Sadamori, L., Staake, T., Santini, S., Revealing household characteristics from smart meter data (2014) Energy, 78, pp. 397-410; Burkhart, S., Unterweger, A., Eibl, G., Engel, D., Detecting swimming pools in 15-min load data (2018) 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), pp. 1651-1655. , IEEE, New York; Chen, D., Barker, S., Subbaswamy, A., Irwin, D., Shenoy, P., Non-intrusive occupancy monitoring using smart meters (2013) Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, pp. 1-8. , ACM, New York; Chen, D., Iyengar, S., Irwin, D., Shenoy, P., Sunspot: Exposing the location of anonymous solar-powered homes (2016) Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments. BuildSys ’16, pp. 85-94. , ACM, New York; Eibl, G., Burkhart, S., Engel, D., Unsupervised holiday detection from low-resolution smart metering data (2018) ICISSP, pp. 477-486. , SciTePress, Setúbal; Eibl, G., Engel, D., Influence of Data Granularity on Smart Meter Privacy (2015) IEEE Trans Smart Grid, 6 (2), pp. 930-939; Goodfellow, I., Bengio, Y., Courville, A., (2016) Deep Learning, , MIT press, Cambridge; Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., Generative adversarial nets (2014) Advances in Neural Information Processing Systems, pp. 2672-2680. , Curran Associates, Inc., Red Hook; Hart, G.W., Nonintrusive Appliance Load Monitoring (1992) Proc IEEE, 80 (12), pp. 1870-1891; Ioffe, S., Szegedy, C., Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015) Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. ICML’15, pp. 448-456. , Microtome Publishing, Brookline; Koch, G., Zemel, R., Salakhutdinov, R., Siamese neural networks for one-shot image recognition (2015) ICML Deep Learning Workshop, vol 2, , Microtome Publishing, Brookline; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, pp. 1097-1105. , Curran Associates, Inc., Red Hook; Lisovich, M.A., Wicker, S.B., Privacy Concerns in Upcoming Residential and Commercial Demand-Response Systems In: IEEE proceedings on Power Systems, 1–10 (2018) New York; Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., Irwin, D., Private memoirs of a smart meter (2010) Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building. BuildSys ’10, pp. 61-66. , ACM, New York; Nair, V., Hinton, G.E., Rectified linear units improve restricted boltzmann machines (2010) Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807-814. , Microtome Publishing, Brookline; (2019) RIS - Rechtsinformationssystem Des Bundes (2019) Rechtsvorschrift für Intelligente Messgeräte-Einführungsverordnung, , https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20007808.Accessed4, May; Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting (2014) J Mach Learn Res, 15, pp. 1929-1958; Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., Attention is all you need (2017) Advances in Neural Information Processing Systems, pp. 5998-6008. , Curran Associates, Inc., Red Hook
PY - 2019/9
Y1 - 2019/9
N2 - The nationwide rollout of smart meters in private households raises privacy concerns: Is it possible to extract privacy-sensitive information from a household’s power consumption? For a small sample of 869 Upper Austrian households, information about consumption-heavy amenities and household characteristics are available. This work studies the detection of households with swimming pools (the most common amenity in the dataset) using Convolutional Neural Networks (CNNs) applied on load heatmaps constructed from load profiles. Although only a small dataset is available, results show that by using CNNs, privacy can be broken automatically, i.e., without the time-consuming, manual feature generation. The method even slightly outperforms a previous approach that relies on a nearest neighbor classifier with engineered features. © 2019, The Author(s).
AB - The nationwide rollout of smart meters in private households raises privacy concerns: Is it possible to extract privacy-sensitive information from a household’s power consumption? For a small sample of 869 Upper Austrian households, information about consumption-heavy amenities and household characteristics are available. This work studies the detection of households with swimming pools (the most common amenity in the dataset) using Convolutional Neural Networks (CNNs) applied on load heatmaps constructed from load profiles. Although only a small dataset is available, results show that by using CNNs, privacy can be broken automatically, i.e., without the time-consuming, manual feature generation. The method even slightly outperforms a previous approach that relies on a nearest neighbor classifier with engineered features. © 2019, The Author(s).
KW - Convolutional neural network
KW - Privacy
KW - Smart metering
KW - Convolution
KW - Electric measuring instruments
KW - Heating
KW - Austrian households
KW - Heatmaps
KW - Privacy concerns
KW - Private households
KW - Sensitive informations
KW - Small samples
KW - Work study
KW - Neural networks
U2 - 10.1186/s42162-019-0097-8
DO - 10.1186/s42162-019-0097-8
M3 - Conference article
SN - 2520-8942
VL - 2
JO - Energy. Inform.
JF - Energy. Inform.
M1 - 10
ER -
SDG 7 Affordable and Clean Energy