Abstract
This work presents a new forecasting algorithm for streaming electricity time series. This algorithm is based on a combination of the K-means clustering algorithm along with both the Naive Bayes classifier and the K nearest neighbors algorithm for regression. In its offline phase it firstly divide data into clusters. Then, the nearest neighbors algorithm is applied for each cluster producing a list of trained regression models, one per each cluster. Finally, a Naive Bayes classifier is trained for predicting the cluster label of an instance using as training the cluster assignments previously generated by K-means. The algorithm is able to be updated incrementally for online learning from data streams. The proposed algorithm has been tested using electricity consumption with a granularity of 10 min for 4-h-ahead predicting. Our algorithm widely overcame other four well-known effective online learners used as benchmark algorithms, achieving the smallest error.
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References
Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. John Wiley, Hoboken (2008)
Martínez-Álvarez, F., Troncoso, A., Asencio-Cortés, G., Riquelme, J.C.: A survey on data mining techniques applied to electricity-related time series forecasting. Energies 8(11), 13162–13193 (2015)
Pérez-Chacón, R., Luna-Romera, J.M., Troncoso, A., Martínez-Álvarez, F., Riquelme, J.C.: Big data analytics for discovering electricity consumption patterns in smart cities. Energies 11, 683 (2018)
Torres, J.F., Galicia, A., Troncoso, A., Martínez-Álvarez, F.: A scalable approach based on deep learning for big data time series forecasting. Integr. Comput.-Aided Eng. 25(4), 335–348 (2018)
Galicia, A., Torres, J.F., Martínez-Álvarez, F., Troncoso, A.: A novel spark-based multi-step forecasting algorithm for big data time series. Inf. Sci. 467, 800–818 (2018)
Laurinec, P., Lucká, M.: Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting. Data Mining Knowl. Disc. 33(2), 413–445 (2018). https://doi.org/10.1007/s10618-018-0598-2
Luo, H., Cai, H., Yu, H., Sun, Y., Bi, Z., Jiang, L.: A short-term energy prediction system based on edge computing for smart city. Fut. Gener. Comput. Syst. 101, 444–457 (2019)
Kwak, Y., Seo, D., Jang, C., Huh, J.-H.: Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data. Energy Build. 57, 250–260 (2013)
Ahmad, T., Chen, H.: A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Sustain. Cities Soc. 54, 102010 (2020)
Gama, J., Rodrigues, P.P.: Stream-based electricity load forecast. In: Proceedings of the Knowledge Discovery in Databases, pp. 446–453 (2007)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)
Aha, D., Kibler, D.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)
Ikonomovska, E., Gama, J., Džeroski, S.: Learning model trees from evolving data streams. Data Mining Knowl. Disc. 23(1), 128–168 (2011)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
Almeida, E., Ferreira, C., Gama, J.: Adaptive model rules from data streams. In: Proceedings of the Machine Learning and Knowledge Discovery in Databases, pp. 480–492 (2013)
Basseville, M.: Detecting changes in signals and systems-a survey. Automatica 24(3), 309–326 (1988)
Bifet, A., Holmes, G., Pfahringer, B., Frank, E.: Fast perceptron decision tree learning from evolving data streams. In: Proceedings of the Advances in Knowledge Discovery and Data Mining, pp. 299–310 (2010)
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Knowledge Discovery on Databases, pp. 97–106 (2001)
Acknowledgements
The authors would like to thank the Spanish Ministry of Science, Innovation and Universities for the support under the project TIN2017-88209-C2-1-R.
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Jiménez-Herrera, P., Melgar-García, L., Asencio-Cortés, G., Troncoso, A. (2020). A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_43
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