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Prediction of Water Usage for Advanced Metering Infrastructure Network with Intelligent Water Meters

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International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023) (CISIS 2023, ICEUTE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 748))

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Abstract

In this article, we present the solution of a prediction algorithm, applied to real world Advanced Network Infrastructure, consisting of intelligent water meters. In the first step of the algorithm time series are passed to outlier detection in order to remove possible disturbing values. The prediction process is carried out with the use of four types of machine learning and deep learning algorithms. The proposed solution based on real world univariate time series taken from multi-family houses is evaluated. The experimental results confirm that the presented solutions are both efficient and flexible.

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References

  1. Billewicz, K.: Smart Metering. PWN (2020)

    Google Scholar 

  2. Blokdyk, G.: Advanced Metering Infrastructure (AMI), 5STARCooks (2022)

    Google Scholar 

  3. Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning forecasting methods: concerns and ways forward. PLoS ONE 13(3), e0194889 (2018)

    Article  Google Scholar 

  4. Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The M4 competition: results, findings, conclusion and way forward. J. Forecast. 34(4), 802–808 (2018)

    Article  Google Scholar 

  5. Fei, T.L., Kai, M.T., Zhi-Hua, Z.: Isolation forest. In: Eighth IEEE International Conference on Data Mining, pp. 413–422 (2008)

    Google Scholar 

  6. Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning fore-casting methods: concerns and ways forward. PLoS ONE 13, e0194889 (2018)

    Article  Google Scholar 

  7. Md Shiblee, P.K., Kalra, B.C.: Time series prediction with multilayer perceptron (MLP): a new generalized error based approach. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5507, pp. 37–44. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03040-6_5

  8. Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2019)

    Article  Google Scholar 

  9. Yoo, Y.: Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches. Knowl.-Based Syst. 178, 74–83 (2019)

    Article  Google Scholar 

  10. Aszemi, N.M., Dominic, P.: Hyperparameter optimization in convolutional neural network using genetic algorithms. Int. J. Adv. Comput. Sci. 10, 269–278 (2019)

    Google Scholar 

  11. Harbola, S., Coors, V.: One dimensional convolutional neural network architectures for wind prediction. Energy Convers. Manage. 195, 70–75 (2019)

    Article  Google Scholar 

  12. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

    Article  Google Scholar 

  13. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  14. Brownlee, J.: Long Short-Term Memory Networks with Python, Develop Sequence Prediction Models With Deep Learning. Machine Learning Mastery (2018)

    Google Scholar 

  15. Cho, K., et al.: Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1724–1734. Association for Computational Linguistics, Doha (2014)

    Google Scholar 

  16. Du, S., Li, T., Yang, Y., Horng, S.J.: Multivariate time series forecasting via attention-based encoder-decoder framework. Neurocomputing 388, 269–279 (2020)

    Article  Google Scholar 

  17. Bryant, M.A., Hesser, T.J., Jensen, R.E.: Evaluation statistics computed for the wave information studies (WIS), Technical Report ERDC/CHL CHETN-I-91 (2016)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Telecommunications and Computer Science, Bydgoszcz University of Science and Technology, Al. Prof. S. Kaliskiego 7, 85-796, Bydgoszcz, Poland

    Łukasz Saganowski & Tomasz Andrysiak

Authors
  1. Łukasz Saganowski
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  2. Tomasz Andrysiak
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Corresponding author

Correspondence to Tomasz Andrysiak .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer and Aerospace Engineering, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Saganowski, Ł., Andrysiak, T. (2023). Prediction of Water Usage for Advanced Metering Infrastructure Network with Intelligent Water Meters. In: García Bringas, P., et al. International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023). CISIS ICEUTE 2023 2023. Lecture Notes in Networks and Systems, vol 748. Springer, Cham. https://doi.org/10.1007/978-3-031-42519-6_12

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