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Electricity price forecasts using a Curvelet denoising based approach

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  • He, Kaijian
  • Xu, Yang
  • Zou, Yingchao
  • Tang, Ling

Abstract

Price movement in the electricity market can be viewed as a nonlinear and dynamic system, exhibiting significant chaotic and multiscale characteristics. To conduct more accurate analysis and forecasting, this paper proposes a new Curvelet denoising based algorithm to analyze these characteristics and predict its future movement. We project the original electricity price into its time delay embedding domain to reveal its chaotic characteristics. The Curvelet denoising method is introduced to separate and suppress the noise disruptions in the transformed phase space. Empirical studies using the typical Australian electricity market prices data show that the proposed algorithm demonstrates more robust and superior performance than the traditional benchmark models.

Suggested Citation

  • He, Kaijian & Xu, Yang & Zou, Yingchao & Tang, Ling, 2015. "Electricity price forecasts using a Curvelet denoising based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 1-9.
    • Handle: RePEc:eee:phsmap:v:425:y:2015:i:c:p:1-9
    DOI: 10.1016/j.physa.2015.01.012
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    1. Michael Graham & Jarno Kiviaho & Jussi Nikkinen, 2013. "Short-term and long-term dependencies of the S&P 500 index and commodity prices," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 583-592, March.
    2. Goeree, Jacob K. & Hommes, Cars H., 2000. "Heterogeneous beliefs and the non-linear cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 761-798, June.
    3. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    4. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    5. Diks, Cees & Hommes, Cars & Zeppini, Paolo, 2013. "More memory under evolutionary learning may lead to chaos," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 808-812.
    6. António Rua, 2011. "A wavelet approach for factor‐augmented forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 666-678, November.
    7. Plourde, André & Watkins, G. C., 1998. "Crude oil prices between 1985 and 1994: how volatile in relation to other commodities?," Resource and Energy Economics, Elsevier, vol. 20(3), pages 245-262, September.
    8. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    9. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    10. Gallegati, Marco & Ramsey, James B. & Semmler, Willi, 2014. "Interest rate spreads and output: A time scale decomposition analysis using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 283-290.
    11. Bigdeli, N. & Afshar, K., 2009. "Chaotic behavior of price in the power markets with pay-as-bid payment mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2560-2569.
    12. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    13. Bigdeli, N. & Afshar, K., 2009. "Characterization of Iran electricity market indices with pay-as-bid payment mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1577-1592.
    14. Rita Sousa & Luís Aguiar-Conraria & Maria Joana Soares, 2014. "Carbon Financial Markets: a time-frequency analysis of CO2 price drivers," NIPE Working Papers 03/2014, NIPE - Universidade do Minho.
    15. Yang, C. W. & Hwang, M. J. & Huang, B. N., 2002. "An analysis of factors affecting price volatility of the US oil market," Energy Economics, Elsevier, vol. 24(2), pages 107-119, March.
    16. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    17. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    18. Gao, Jianbo & Hu, Jing & Mao, Xiang & Tung, Wen-wen, 2012. "Detecting low-dimensional chaos by the “noise titration” technique: Possible problems and remedies," Chaos, Solitons & Fractals, Elsevier, vol. 45(3), pages 213-223.
    19. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    20. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
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    Cited by:

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    2. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    3. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, vol. 9(8), pages 1-27, August.
    4. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
    5. Bartoš, Erik & Pinčák, Richard, 2017. "Identification of market trends with string and D2-brane maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 57-70.
    6. Ziyang Wang & Masahiro Mae & Takeshi Yamane & Masato Ajisaka & Tatsuya Nakata & Ryuji Matsuhashi, 2024. "Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration," Energies, MDPI, vol. 17(11), pages 1-17, June.
    7. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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