Abstract
Short term electricity load forecasting is one of the main concerns for electricity producers in regular system planning, where electricity demand is influenced by the day type among other factors that must be identified before modeling to ensure good load balance. This paper proposes a two-stage approach for identifying day types based on an image of the daily load curve. In the first stage, a set of day classes of load profiles using K-Means clustering algorithm is created, while in the second stage, the Time-Series Visualization method is used to build a classification model able to assign different days to the existing classes, detecting visual characteristics from daily load data curves. This classification model could be used in the forecasting process either by including the day-type as an input or by modeling each day-type independently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kyriakides, E., Polycarpou, M.: Short term electric load forecasting: a tutorial. In: Chen, K., Wang, L. (eds.) Trends in Neural Computation. SCI, vol. 35, pp. 391–418. Springer, Heidelberg (2007)
Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001)
Fay, D., Ringwood, J.V., Condon, M., Kelly, M.: 24-h electrical load dataa sequential or partitioned time series? Neurocomputing 55(3), 469–498 (2003)
Dai, W., Wang, P.: Application of pattern recognition and artificial neural network to load forecasting in electric power system. In: Third International Conference on Natural Computation, ICNC 2007, vol. 1, pp. 381–385. IEEE (2007)
Senjyu, H.S., Senjyu, T., Sakihara, H., Tamaki, Y., Uezato, K.: Next-day load curve forecasting using neural network based on similarity. Electr. Power Compon. Syst. 29(10), 939–948 (2001)
Li, R., Gu, C., Li, F., Shaddick, G., Dale, M.: Development of low voltage network templatespart i: substation clustering and classification. IEEE Trans. Power Syst. 30(6), 3036–3044 (2015)
Hernández, L., Baladrón, C., Aguiar, J.M., Carro, B., Sánchez-Esguevillas, A.: Classification and clustering of electricity demand patterns in industrial parks. Energies 5(12), 5215–5228 (2012)
Khadir, M.T., Fay, D., Boughrira, A.: Day type identification for algerian electricity load using kohonen maps. Trans. Eng. Comput. Technol. 15, 296–300 (2006)
Tarek, K.M., Farouk, B., Sofiane, K.: Kohonen Maps Combined to K-means in a Two Level Strategy for Time Series ClusteringApplication to Meteorological and Electricity Load Data. INTECH Open Access Publisher (2010)
Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn. Lett. 42, 11–24 (2014)
Qian, C., Wang, Y., Guo, L.: A novel method based on data visual autoencoding for time-series classification. In: Deng, Z., Li, H. (eds.) Proceedings of the 2015 Chinese Intelligent Automation Conference. LNEE, vol. 336, pp. 97–104. Springer, Heidelberg (2015)
Lloyd, S.P.: Least squares quantization in pcm. IEEE Trans. Inf. Theor. 28(2), 129–137 (1982)
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pretraining help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine learning, pp. 1096–1103. ACM (2008)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)
Acknowledgments
We would like to thank Sonelgaz (Algeria’s national electricity and gas company) for providing three years of electricity data for this project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Farfar, K.E., Khadir, M.T. (2016). Day Types Identification of Algerian Electricity Load Using an Image Based Two-Stage Approach. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-44781-0_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44780-3
Online ISBN: 978-3-319-44781-0
eBook Packages: Computer ScienceComputer Science (R0)