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Short-Term Load Forecasting Using Random Forests

  • Conference paper
Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

This study proposes using a random forest model for short-term electricity load forecasting. This is an ensemble learning method that generates many regression trees (CART) and aggregates their results. The model operates on patterns of the time series seasonal cycles which simplifies the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and multiple seasonal cycles. The main advantages of the model are its ability to generalization, built-in cross-validation and low sensitivity to parameter values. As an illustration, the proposed forecasting model is applied to historical load data in Poland and its performance is compared with some alternative models such as CART, ARIMA, exponential smoothing and neural networks. Application examples confirm good properties of the model and its high accuracy.

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References

  1. Taylor, J.W., Snyder, R.D.: Forecasting Intraday Data with Multiple Seasonal Cycles Using Parsimonious Seasonal Exponential Smoothing. Omega 40(6), 748–757 (2012)

    Article  Google Scholar 

  2. Weron, R.: Modeling and Forecasting Electricity Loads and Prices. Wiley (2006)

    Google Scholar 

  3. Kodogiannis, V.S., Anagnostakis, E.M.: Soft Computing Based Techniques for Short-Term Load Forecasting. Fuzzy Sets and Systems 128, 413–426 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Cheng, Y.-Y., Chan, P.P.K., Qiu, Z.-W.: Random Forest Based Ensemble System for Short Term Load Forecasting. Proc. Machine Learning and Cybernetics (ICMLC) 1, 52–56 (2012)

    Google Scholar 

  5. Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  6. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman and Hall (1984)

    Google Scholar 

  7. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer (2009)

    Google Scholar 

  8. Dudek, G.: Similarity-based Approaches to Short-Term Load Forecasting. In: Zhu, J.J., Fung, G.P.C. (eds.) Forecasting Models: Methods and Applications, pp. 161–178. Concept Press (2010)

    Google Scholar 

  9. Dudek, G.: Short-Term Load Forecasting Using Fuzzy Regression Trees. Przegląd Elektrotechniczny (Electrical Review) 90(4), 108–111 (2014) (in Polish)

    Google Scholar 

  10. Dudek, G.: Forecasting Time Series with Multiple Seasonal Cycles using Neural Networks with Local Learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 52–63. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  11. Hyndman, R.J., Khandakar, Y.: Automatic Time Series Forecasting: The Forecast Package for R. Journal of Statistical Software 27(3), 1–22 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Czestochowa University of Technology, Al. Armii Krajowej 17, 42-200, Czestochowa, Poland

    Grzegorz Dudek

Authors
  1. Grzegorz Dudek
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Corresponding author

Correspondence to Grzegorz Dudek .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

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© 2015 Springer International Publishing Switzerland

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Dudek, G. (2015). Short-Term Load Forecasting Using Random Forests. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_71

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_71

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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