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Using local learning with fuzzy transform: application to short term forecasting problems

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Abstract

In this paper, we formally discuss a computational scheme, which combines a local weighted regression model with fuzzy transform (or F-transform for short). The latter acts as a reduction technique on the cardinality of the learning problem, resulting in a more efficient algorithm. We tested the proposed approach first through two typical benchmark problems, that is the Hénon and the Mackey–Glass chaotic time series, then we applied it to short-term forecasting problems. Short-term forecasting is important in the energy field for the management of power systems and for energy trading. Hence, we considered two typical application examples in this field, that is wind power forecasting and load forecasting. Numerical results show the effectiveness of the proposed approach through a comparison against alternative techniques.

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References

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.

    Article  MathSciNet  Google Scholar 

  • Appice, A., Pravilovic, S., Lanza, A., & Malerba, D. (2015). Very short-term wind speed forecasting using spatio-temporal lazy learning, lecture notes in computer science, 9356 (pp. 9–16).

  • Bessec, M., & Fouquau, J. (2018). Short-run electricity load forecasting with combinations of stationary wavelet transforms. European Journal of Operational Research, 264, 149–164.

    Article  MathSciNet  Google Scholar 

  • Bontempi, G. (1999). Local learning techniques for modelling, prediction and control. IRIDIA - Université Libre de Bruxelles, (Ph.D. thesis).

  • Bontempi, G., Bersini, H., & Birattari, M. (2001). The local paradigm for modeling and control: From neuro-fuzzy to lazy learning. Fuzzy Sets and Systems, 121(1), 59–72.

    Article  MathSciNet  Google Scholar 

  • Bontempi, G., Birattari, M., & Bersini, H. (1999). Local learning for iterated time-series prediction. In I. Bratko, S. Dzeroski (Eds.), Machine learning: Proceedings of the sixteenth international conference (pp. 32–38). San Francisco, CA: Morgan Kaufmann.

  • Capuno, M., Kim, J. S., & Song, H. (2017). Very short-term load forecasting using hybrid algebraic prediction and support vector regression. Mathematical Problems in Engineering (Article ID 8298531).

  • Chan, S. C., Tsui, K. M., Wu, H. C., Hou, Y., Wu, Y. C., & Wu, F. F. (2012). Load/price forecasting and managing demand-response for smart grids. IEEE Signal Processing Magazine, 29(5), 68–85.

    Article  Google Scholar 

  • Chang, L. C., Chang, F. J., & Wang, Y. P. (2009). Auto-configuring radial basis function networks for chaotic time series and flood forecasting. Hydrological Processes, 23, 2450–2459.

    Article  Google Scholar 

  • Fan, S., & Hyndman, R. J. (2012). Short-term load forecasting based on a semi-parametric additive model. IEEE Transactions on Power Systems, 27(1), 134–141.

    Article  Google Scholar 

  • Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16(1), 44–55.

    Article  Google Scholar 

  • Hong, T., & Wang, P. (2014). Fuzzy interaction regression for short term load forecasting. Fuzzy Optimization and Decision Making, 13(1), 91–103.

    Article  Google Scholar 

  • Hong, T., Wang, P., & Willis, H. L. (2011). A naive multiple linear regression benchmark for short term load forecasting. In IEEE Power and Energy Society General Meeting (pp. 1–6). Detroit, MI, USA, 2011.

  • Hurtik, P., & Tomasiello, S. (2019). A review on the application of fuzzy transform in data and image compression. Soft Computing(in press).

  • Hurvich, C. M., Simonoff, J. S., & Tsai, C. L. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 60(2), 271–293.

    Article  MathSciNet  Google Scholar 

  • Jayawardena, A. W., Xu, P. C., Tsang, F. L., & Li, W. K. (2006). Determining the structure of a radial basis function network for prediction of nonlinear hydrological time series. Hydrological Sciences Journal, 51(1), 21–44.

    Article  Google Scholar 

  • Kuck, M., & Scholz-Reiter, B. (2013). A genetic algorithm to optimize lazy learning parameters for the prediction of customer demands. In 12th international conference on machine learning and applications (pp. 160–165).

  • Loia, V., Tomasiello, S., & Vaccaro, A. (2017). Using fuzzy transform in multi-agent based monitoring of smart grids. Information Science, 388(389), 209–224.

    Article  Google Scholar 

  • Meng, K., Dong, Z. Y., & Wong, K. P. (2009). Self-adaptive radial basis function neural network for short-term electricity price forecasting. IET Generation, Transmission & Distribution, 3(4), 325–335.

    Article  Google Scholar 

  • Mishra, S. P., & Dash, P. K. (2017). Short-term prediction of wind power using a hybrid pseudo-inverse Legendre neural network and adaptive firefly algorithm. Neural Conputing and Applications, 31(7), 2243–2268.

    Article  Google Scholar 

  • Pan, T. H., Li, S., & Li, N. (2009). Optimal bandwidth design for lazy learning via particle swarm optimization. Intelligent Automation & Soft Computing, 15(1), 1–11.

    Article  MathSciNet  Google Scholar 

  • Poncela, M., Poncela, P., & Peran, J. R. (2013). Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting. Applied Energy, 108, 349–362.

    Article  Google Scholar 

  • Rana, M., & Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118–132.

    Article  Google Scholar 

  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society B, 36(1), 111–147.

    MathSciNet  MATH  Google Scholar 

  • Sun, G., et al. (2018). Short-term wind power forecasts by a synthetical similar time series data mining method. Renewable Energy, 115, 575–584.

    Article  Google Scholar 

  • Taylor, J. W. (2008). An evaluation of methods for very short-term load forecasting using minute-by-minute British data. International Journal of Forecasting, 24, 645–658.

    Article  Google Scholar 

  • Vaccaro, A., Mercogliano, P., Schiano, P., & Villacci, D. (2011). An adaptive framework based on multi-model data fusion for one-day-ahead wind power forecasting. Electric Power Systems Research, 81, 775–782.

    Article  Google Scholar 

  • Zhang, P., Gao, B. J., Zhu, X., & Guo, L. (2011). Enabling fast lazy learning for data streams. In IEEE international conference on data mining (ICDM) (pp. 932–941).

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Authors and Affiliations

  1. DISA-MIS, University of Salerno, Fisciano, Italy

    Vincenzo Loia

  2. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Stefania Tomasiello

  3. Engineering Department, University of Sannio, Benevento, Italy

    Alfredo Vaccaro

  4. School of Information, Renmin University of China, Beijing, China

    Jinwu Gao

Authors
  1. Vincenzo Loia
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  2. Stefania Tomasiello
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  3. Alfredo Vaccaro
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  4. Jinwu Gao
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Correspondence to Stefania Tomasiello.

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Loia, V., Tomasiello, S., Vaccaro, A. et al. Using local learning with fuzzy transform: application to short term forecasting problems. Fuzzy Optim Decis Making 19, 13–32 (2020). https://doi.org/10.1007/s10700-019-09311-x

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  • DOI: https://doi.org/10.1007/s10700-019-09311-x

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