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ANN-Based Electricity Price Forecasting Under Special Consideration of Time Series Properties

  • Jan-Hendrik MeierEmail author
  • Stephan Schneider
  • Iwana Schmidt
  • Philip Schüller
  • Thies Schönfeldt
  • Bastian Wanke
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1007)

Abstract

If one examines the spot price series of electrical power over the course of time, it is striking that the electricity price across the day takes a course that is determined by power consumption following a day and night rhythm. This daily course changes in its height and temporal extent in both, the course of the week, as well as with the course of the year. This study deals methodologically with non-linear correlative and autocorrelative time series properties of the electricity spot price. We contribute the usage of non-fully connectionist networks in relation to fully connectionist networks to decompose non-linear correlative time series properties. Additionally, we contribute the usage of long short-term-memory network (LSTM) to discover and to deal with autocorrelation effects.

Keywords

Electricity prices Artificial neural network LSTM ARIMAX 

References

  1. Adebiyi, A.A., Adewumi, A.O., Ayo, C.K.: Comparison of ARIMA and artificial neural networks models for stock price prediction. J. Appl. Math. 2014, 1–7 (2014)MathSciNetCrossRefGoogle Scholar
  2. Barrow, D., Kourentzes, N.: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days. Eur. J. Oper. Res. 264(3), 967–977 (2018)MathSciNetCrossRefGoogle Scholar
  3. Bierbrauer, M., Menn, C., Rachev, S.T., Trück, S.: Spot and derivative pricing in the EEX power market. J. Bank. Finance 31(11), 3462–3485 (2007)CrossRefGoogle Scholar
  4. Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1971). (2. printing)zbMATHGoogle Scholar
  5. Conejo, A.J., Plazas, M.A., Espinola, R., Molina, A.B.: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst. 20(2), 1035–1042 (2005a)CrossRefGoogle Scholar
  6. Conejo, A.J., Contreras, J., Espínola, R., Plazas, M.A.: Forecasting electricity prices for a day-ahead pool-based electric energy market. Int. J. Forecast. 21(3), 435–462 (2005b)CrossRefGoogle Scholar
  7. Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)CrossRefGoogle Scholar
  8. Dudek, G.: Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting. Int. J. Forecast. 32(3), 1057–1060 (2016)CrossRefGoogle Scholar
  9. Filho, J.C.R., de Affonso, C.M., de Oliviera, R.C.L.: Energy price prediction multi-step ahead using hybrid model in the Brazilian market. Electric Power Syst. Res. 117, 115–122 (2014)CrossRefGoogle Scholar
  10. Gajowniczek, K., Ząbkowski, T.: Short term electricity forecasting using individual smart meter data. Procedia Comput. Sci. 35, 589–597 (2014)CrossRefGoogle Scholar
  11. Garcia, R.C., Contreras, J., van Akkeren, M., Garcia, J.B.C.: A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans. Power Syst. 20(2), 867–874 (2005)CrossRefGoogle Scholar
  12. Ghiassi, M., Saidane, H., Zimbra, D.K.: A dynamic artificial neural network model for forecasting time series events. Int. J. Forecast. 21(2), 341–362 (2005)CrossRefGoogle Scholar
  13. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5), 602–610 (2005)CrossRefGoogle Scholar
  14. Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)MathSciNetCrossRefGoogle Scholar
  15. Haykin, S.S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education, Upper Saddle River (2009)Google Scholar
  16. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  17. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79(8), 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  18. Hu, Z., Yang, L., Wang, Z., Gan, D., Sun, W., Wang, K.: A game-theoretic model for electricity markets with tight capacity constraints. Int. J. Electric. Power Energy Syst. 30(3), 207–215 (2008)CrossRefGoogle Scholar
  19. Hyndman, R.J., Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 27(3) (2008)Google Scholar
  20. Isa, A.M., Niimura, T., Sakamoto, N., Ozawa, K., Yokoyama, R.: Electricity market forecasting using artificial neural network models optimized by grid computing. IFAC Proc. 42(9), 273–277 (2009)CrossRefGoogle Scholar
  21. Knittel, C.R., Roberts, M.R.: An empirical examination of restructured electricity prices. Energy Econ. 27(5), 791–817 (2005)CrossRefGoogle Scholar
  22. Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841–851 (2017)CrossRefGoogle Scholar
  23. Koopman, S.J., Ooms, M., Carnero, M.A.: Periodic seasonal reg-ARFIMA–GARCH models for daily electricity spot prices. J. Am. Stat. Assoc. 102(477), 16–27 (2007)MathSciNetCrossRefGoogle Scholar
  24. Koutroumandis, T., Ioannou, K., Arabatzis, G.: Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA–ANN model. Energy Policy 37(9), 3627–3634 (2009)CrossRefGoogle Scholar
  25. Krzemien, A., Riesgo Fernández, P., Suárez Sánchez, A., Sánchez Lasheras, F.: Forecasting European thermal coal spot prices. J. Sustain. Min. 14(4), 203–210 (2015)CrossRefGoogle Scholar
  26. Lago, J., de Ridder, F., Vrancx, P., de Schutter, B.: Forecasting day-ahead electricity prices in Europe: the importance of considering market integration. Appl. Energy 211, 890–903 (2018)CrossRefGoogle Scholar
  27. Maciejowska, K., Nowotarski, J., Weron, R.: Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging. Int. J. Forecast. 32(3), 957–965 (2016)CrossRefGoogle Scholar
  28. Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series (2015)Google Scholar
  29. Mandal, P., Senjyu, T., Funabashi, T.: Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market. Energy Convers. Manag. 47(15), 2128–2142 (2006)CrossRefGoogle Scholar
  30. Maniatis, P.: A taxonomy of electricity demand forecasting techniques and a selection strategy. Int. J. Manag. Excel. 8(2), 881 (2017)Google Scholar
  31. Marcjasz, G., Uniejewski, B., Weron, R.: On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks. Int. J. Forecast. 34(1) (2018)Google Scholar
  32. Marín, J.B., Orozco, E.T., Velilla, E.: Forecasting electricity price in colombia: a comparison between neural network, ARMA process and hybrid models. Int. J. Energy Econ. Policy 8(3), 10 (2018)Google Scholar
  33. Mirakyan, A., Meyer-Renschhausen, M., Koch, A.: Composite forecasting approach, application for next-day electricity price forecasting. Energy Econ. 66, 228–237 (2017)CrossRefGoogle Scholar
  34. Misiorek, A., Trueck, S., Weron, R.: Point and interval forecasting of spot electricity prices: linear vs. non-linear time series models. Stud. Nonlinear Dyn. Econom. 10(3) (2006)Google Scholar
  35. Panapakidis, I.P., Dagoumas, A.S.: Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl. Energy 172, 132–151 (2016)CrossRefGoogle Scholar
  36. Psaradellis, I., Sermpinis, G.: Modelling and trading the US implied volatility indices. Evidence from the VIX, VXN and VXD indices. Int. J. Forecast. 32(4), 1268–1283 (2016)CrossRefGoogle Scholar
  37. Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30(4), 1030–1081 (2014)CrossRefGoogle Scholar
  38. Yamashita, D., Isa, A.M., Yokoyama, R., Niimura, T.: Forecasting of electricity price and demand using autoregressive neural networks. IFAC Proc. 41(2), 14934–14938 (2008)CrossRefGoogle Scholar
  39. Yamin, H., Shahidehpour, S., Li, Z.: Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets. Int. J. Electr. Power Energy Syst. 26(8), 571–581 (2004)CrossRefGoogle Scholar
  40. Zareipour, H., Canizares, C.A., Bhattacharya, K., Thomson, J.: Application of public-domain market information to forecast Ontario’s wholesale electricity prices. IEEE Trans. Power Syst. 21(4), 1707–1717 (2006)CrossRefGoogle Scholar
  41. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)CrossRefGoogle Scholar
  42. Zhang, G., Patuwo, E.B., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)CrossRefGoogle Scholar
  43. Zhou, M., Yan, Z., Ni, Y.X., Li, G., Nie, Y.: Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach. IEE Proc. - Gener. Transm. Distrib. 153(2), 187 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Kiel University of Applied SciencesKielGermany

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