Abstract
Smart grids can optimize their energy management by analyzing data collected from all processes of power utilization in smart cities. Typical smart grids consist of diverse systems such as energy management system and renewable energy system. In order to use such systems efficiently, accurate load forecasting should be carried out. However, if there are many anomalies in the data used to construct the predictive model, the accuracy of the prediction will inevitably decrease. Many statistical methods proposed for anomaly detection have had difficulty in reflecting seasonality. Hence, in this chapter, we propose VAE (Variational AutoEncoder)-based scheme for accurate anomaly detection. We construct diverse artificial neural network-based load forecasting models using different combinations of anomaly detection and data interpolation, and then compare their performance. Experimental results show that using VAE-based anomaly detection with a random forest-based data interpolation shows the best performance.
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References
S. Jagannathan, Real-time big data analytics architecture for remote sensing application, in 2016 International Conference on Signal Processing, Communication, Power and Embedded System, (2016), pp. 1912–1916
D. Kyriazis, T. Varvarigou, D. White, A. Rossi, J. Cooper, Sustainable smart city IoT applications: Heat and electricity management & Eco-conscious cruise control for public transportation, in 2013 IEEE 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks”, (2013), pp. 1–5
J.S. Chou, N.T. Ngo, Smart grid data analytics framework for increasing energy savings in residential buildings. Autom. Constr. 72, 247–257 (2016)
S. Saponara, R. Saletti, L. Mihet-Popa, Hybrid micro-grids exploiting renewables sources, battery energy storages, and bi-directional converters. Appl. Sci. 9, 4973–4990 (2019)
M. Raciti, S. Nadjm-Tehrani, Embedded cyber-physical anomaly detection in smart meters, in Critical information infrastructures security, (2013), pp. 34–45
C. Wang, K. Viswanathan, L. Choudur, V. Talwar, W. Satterfield, K. Schwan, Statistical techniques for online anomaly detection in data centers, in 12th IFIP/IEEE International Symposium on Integrated Network Management and Workshops, (2011), pp. 385–392
S. Park, J. Moon, S. Jung, S. Rho, S.W. Baik, E. Hwang, A two-stage industrial load forecasting scheme for day-ahead combined cooling, heating and power scheduling. Energies 13, 443–465 (2020)
J. Moon, Y. Kim, M. Son, E. Hwang, Hybrid short-term load forecasting scheme using random forest and multilayer perceptron. Energies 11, 3283–3302 (2018)
M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks. AICHE J. 37, 233–243 (1991)
D.P. Kingma, M. Welling, Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
J. An, S. Cho, Variational autoencoder based anomaly detection using reconstruction probability, in Special Lecture on IE, vol. 2, (2015)
Y. Liang, D. Niu, W.C. Hong, Short term load forecasting based on feature extraction and improved general regression neural network model. Energy 166, 653–663 (2019)
J. Moon, S. Park, S. Rho, E. Hwang, A comparative analysis of artificial neural network architectures for building energy consumption forecasting. Int. J. Distrib. Sens. Netw. 15(9), 155014771987761 (2019)
S. Karsoliya, Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int. J. Eng. Trends Technol. 3(6), 714–717 (2012)
Acknowledgement
This research was supported in part by National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (grand number: 2019M3F2A1073179) and in part by the Korea Electric Power Corporation (grant number: R18XA05).
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Park, S., Jung, S., Hwang, E., Rho, S. (2021). Variational AutoEncoder-Based Anomaly Detection Scheme for Load Forecasting. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_62
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DOI: https://doi.org/10.1007/978-3-030-70296-0_62
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