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Regular Paper | Open Access

A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting

Haoran Zhang1, Weihao Hu1 ( ), Di Cao1, Qi Huang1, Zhe Chen2, Frede Blaabjerg2
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
Department of Energy Technology, Aalborg University, Aalborg, Denmark
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Abstract

Electricity prices have complex features, such as high frequency, multiple seasonality, and nonlinearity. These factors will make the prediction of electricity prices difficult. However, accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies. To improve the accuracy of prediction by using each algorithms' advantages, this paper proposes a hybrid model that uses the Empirical Mode Decomposition (EMD), Autoregressive Integrated Moving Average (ARIMA), and Temporal Convolutional Network (TCN). EMD is used to decompose the electricity prices into low and high frequency components. Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model. Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland (PJM) electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

Keywords

Autoregressive integrated moving average modelelectricity price forecastingempirical mode decompositiontemporal convolutional network

References

[1]

O. Abedinia, M. Zareinejad, M. H. Doranehgard, G. Fathi, and N. Ghadimi, “Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach,” Journal of Cleaner Production, vol. 215, pp. 878–889, Apr. 2019.
Crossref Google Scholar
[2]

Y. Qin, L. Wu, J. Zheng, M. Li, Z. Jing, Q. H. Wu, X. Zhou, F. Wei, “Optimal operation of integrated energy systems subject to coupled demand constraints of electricity and natural gas,” CSEE Journal of Power and Energy Systems, vol. 6, no. 2, pp. 444–457, Jun. 2020.
Google Scholar
[3]

H. Khodaei, M. Hajiali, A. Darvishan, M. Sepehr, and N. Ghadimi, “Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming,” Applied Thermal Engineering, vol. 137, pp. 395–405, Jun. 2018.
Crossref Google Scholar
[4]

H. A. Bagal, Y. N. Soltanabad, M. Dadjuo, K. Wakil, and N. Ghadimi, “RETRACTED: Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory,” Solar Energy, vol. 169, pp. 343–352, Jul. 2018.
Crossref Google Scholar
[5]

N. Ghadimi, A. Akbarimajd, H. Shayeghi, and O. Abedinia, “Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting,” Energy, vol. 161, pp. 130–142, Oct. 2018.
Crossref Google Scholar
[6]

M. Vahedipour-Dahraie, H. Rashidizadeh-Kermani, A. Anvari-Moghaddam and P. Siano, “Flexible stochastic scheduling of microgrids with islanding operations complemented by optimal offering strategies,” CSEE Journal of Power and Energy Systems, vol. 6, no. 4, pp. 867–877, Dec. 2020.
Google Scholar
[7]

M. Saeedi, M. Moradi, M. Hosseini, A. Emamifar, and N. Ghadimi, “Robust optimization based optimal chiller loading under cooling demand uncertainty,” Applied Thermal Engineering, vol. 148, pp. 1081–1091, Feb. 2019.
Crossref Google Scholar
[8]

D. Cao, W. H. Hu, J. B. Zhao, G. Z. Zhang, B. Zhang, Z. Liu, Z. Chen, and F. Blaabjerg. “Reinforcement learning and its applications in modern power and energy systems: a review,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1029–1042, Nov. 2020.
Crossref Google Scholar
[9]

S. Kwon, S. H. Cho, R. K. Roberts, H. J. Kim, K. Park, and T. H. E. Yu, “Short-run and the long-run effects of electricity price on electricity intensity across regions,” Applied Energy, vol. 172, pp. 372–382, Jun. 2016.
Crossref Google Scholar
[10]

G. Z. Zhang, W. H. Hu, D. Cao, Q. Huang, J. B. Yi, Z. Chen, and F. Blaabjerg, “Deep reinforcement learning-based approach for proportional resonance power system stabilizer to prevent ultra-low-frequency oscillations,” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5260–5272, Nov. 2020.
Crossref Google Scholar
[11]

D. Cao, W. H. Hu, J. B. Zhao, Q. Huang, Z. Chen, and F. Blaabjerg, “A multi-agent deep reinforcement learning based voltage regulation using coordinated PV inverters,” IEEE Transactions on Power Systems, vol. 35, no. 5, pp. 4120–4123, Sep. 2020.
Crossref Google Scholar
[12]

O. Abedinia, N. Amjady, and H. Zareipour, “A new feature selection technique for load and price forecast of electrical power systems,” IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 62–74, Jan. 2017.
Crossref Google Scholar
[13]

Z. F. Tan, J. L. Zhang, J. H. Wang, and J. Xu, “Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models,” Applied Energy, vol. 87, no. 11, pp. 3606–3610, Nov. 2010.
Crossref Google Scholar
[14]

W. Gao, A. Darvishan, M. Toghani, M. Mohammadi, O. Abedinia, and N. Ghadimi, “Different states of multi-block based forecast engine for price and load prediction,” International Journal of Electrical Power & Energy Systems, vol. 104, pp. 423–435, Jan. 2019.
Crossref Google Scholar
[15]

M. Rafiei, T. Niknam, and M. H. Khooban, “Probabilistic forecasting of hourly electricity price by generalization of ELM for usage in improved wavelet neural network,” IEEE Transactions on Industrial Informatics, vol. 13, no. 1, pp. 71–79, Feb. 2017.
Crossref Google Scholar
[16]

M. Ghayekhloo, R. Azimi, M. Ghofrani, M. B. Menhaj, and E. Shekari, “A combination approach based on a novel data clustering method and Bayesian recurrent neural network for day-ahead price forecasting of electricity markets,” Electric Power Systems Research, vol. 168, pp. 184–199, Mar. 2019.
Crossref Google Scholar
[17]

W. M. Lin, H. J. Gow, and M. T. Tsai, “An enhanced radial basis function network for short-term electricity price forecasting,” Applied Energy, vol. 87, no. 10, pp. 3226–3234, Oct. 2010.
Crossref Google Scholar
[18]

C. W. Gao, E. Bompard, R. Napoli, and H. Z. Cheng, “Price forecast in the competitive electricity market by support vector machine,” Physica A: Statistical Mechanics and its Applications, vol. 382, no. 1, pp. 98–113, Aug. 2007.
Crossref Google Scholar
[19]

Y. Zhang, C. Li, and L. Li, “Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods,” Applied Energy, vol. 190, pp. 291–305, Mar. 2017.
Crossref Google Scholar
[20]

H. Y. Cheng, X. W. Ding, W. N. Zhou, and R. Q. Ding, “A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange,” International Journal of Electrical Power & Energy Systems, vol. 110, pp. 653–666, Sep. 2019.
Crossref Google Scholar
[21]
A. E. K., M. Eghlimi, and Z. J. Zhang, “Forecasting the electricity price in iran power market: a comparison between neural networks and time series methods,” in 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Hong Kong, China, 2014, pp. 1–6.
[22]

S. Anbazhagan and N. Kumarappan, “Day-ahead deregulated electricity market price forecasting using recurrent neural network,” IEEE Systems Journal, vol. 7, no. 4, pp. 866–872, Dec. 2013.
Crossref Google Scholar
[23]

K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “LSTM: a search space odyssey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2222–2232, Oct. 2017.
Crossref Google Scholar
[24]
S. J. Bai, J. Z. Kolter, and V. Koltun. “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv: 1803.01271, 2018.
[25]

R. Z. Wan, S. P. Mei, J. Wang, M. Liu, and F. Yang, “Multivariate temporal convolutional network: a deep neural networks approach for multivariate time series forecasting,” Electronics, vol. 8, no. 8, pp. 876, Aug. 2019.
Crossref Google Scholar
[26]
S. N. Dodamani, V. J. Shetty, and R. B. Magadum, “Short term load forecast based on time series analysis: a case study,” in 2015 International Conference on Technological Advancements in Power and Energy (TAP Energy), Kollam, 2015, pp. 299–303.
Crossref
[27]

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. A. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, Mar. 1998.
Crossref Google Scholar
[28]
E. Dokur, M. Kurban, and S. Ceyhan, “Hybrid model for short term wind speed forecasting using empirical mode decomposition and artificial neural network,” in 2015 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, 2015, pp. 420–423.
Crossref
[29]

J. Tang, T. Y. Chai, W. Yu, Z. Liu, and X. J. Zhou, “A comparative study that measures ball mill load parameters through different single-scale and multiscale frequency spectra-based approaches,” IEEE Transactions on Industrial Informatics, vol. 12, no. 6, pp. 2008–2019, Dec. 2016.
Crossref Google Scholar
[30]
M. Xie, C. Sandels, K. Zhu, and L. Nordström, “A seasonal ARIMA model with exogenous variables for elspot electricity prices in Sweden,” 2013 10th International Conference on the European Energy Market (EEM), pp. 1–4, 2013.
Crossref
[31]
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
[32]
D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” arXiv: 1412.6980, 2014.
[33]
A. C. Wilson, R. Roelofs, M. Stern, N. Srebro, and B. Recht, “The marginal value of adaptive gradient methods in machine learning,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, pp. 4151–4161.
[34]
Website of PJM electricity market.[Online]. Available: www.pjm.com.
[35]

S. K. Aggarwal, L. M. Saini, and A. Kumar, “Electricity price forecasting in deregulated markets: a review and evaluation,” International Journal of Electrical Power & Energy Systems, vol. 31, no. 1, pp. 13–22, Jan. 2009.
Google Scholar
[36]

D. Z. Yang, E. Wu, and J. Kleissl, “Operational solar forecasting for the real-time market,” International Journal of Forecasting, vol. 35, no. 4, pp. 1499–1519, Oct./Dec. 2019.
Crossref Google Scholar
[37]

A. J. Conejo, J. Contreras, R. Espínola, and M. A. Plazas, “Forecasting electricity prices for a day-ahead pool-based electric energy market,” International Journal of Forecasting, vol. 21, no. 3, pp. 435–462, Jul./Sep. 2005.
Crossref Google Scholar
CSEE Journal of Power and Energy Systems
Volume 10 Issue 3,
May 2024
Pages 1119-1130
DOI: 10.17775/CSEEJPES.2020.04810
Cite this article:
Zhang H, Hu W, Cao D, et al. A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1119-1130. https://doi.org/10.17775/CSEEJPES.2020.04810

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Received: 04 September 2020
Revised: 25 December 2020
Accepted: 01 October 2021
Published: 10 September 2021
© 2020 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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