Abstract
Electricity is a product that has greatly changed the way we think about the world. In fact, it is one of the indicators that show the progress of a civilisation. With the deregulation of the electricity market in the 1990s, electricity price forecasting became a fundamental task in all countries. This task was a challenge for both producers and consumers, as many factors had to be taken into account. In this paper, given the large number of factors that influence the electricity market price we have conducted several experiments to see how the use of multiple variables can improve the effectiveness of price prediction. Tests have been performed with different time periods using data from Spain and different Deep Learning models, showing that the use of these variables represents an improvement with respect to the univariate model, in which only the price was considered.
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References
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2017)
Atef, S., Eltawil, A.B.: A comparative study using deep learning and support vector regression for electricity price forecasting in smart grids. In: 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), pp. 603–607. IEEE (2019)
Cruz, A., Muñoz, A., Zamora, J.L., Espínola, R.: The effect of wind generation and weekday on Spanish electricity spot price forecasting. Electric Power Syst. Res. 81(10), 1924–1935 (2011)
Red Eléctrica de España, S.A.U.R.: esios: Sistema de información del operador del sistema (2020). https://www.esios.ree.es
Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)
González, C., Mira-McWilliams, J., Juárez, I.: Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, bagging and random forests. IET Generation, Transmission Distribution 9(11), 1120–1128 (2015)
He, H., Lu, N., Jiang, Y., Chen, B., Jiao, R.: End-to-end probabilistic forecasting of electricity price via convolutional neural network and label distribution learning. Energy Rep. 6, 1176–1183 (2020)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, C.J., Shen, Y., Chen, Y.H., Chen, H.C.: A novel hybrid deep neural network model for short-term electricity price forecasting. Int. J. Energy Res. 45(2), 2511–2532 (2021)
Jahangir, H., et al.: A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks. IEEE Trans. Industr. Inf. 16(4), 2369–2381 (2019)
Jahangir, H., et al.: A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks. IEEE Trans. Industr. Inf. 16(4), 2369–2381 (2020). https://doi.org/10.1109/TII.2019.2933009
Lara-Benıtez, P., Carranza-Garcıa, M., Riquelme, J.C.: An experimental review on deep learning architectures for time series forecasting (2020)
Oord, A.v.d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., Kavukcuoglu, K.: Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)
País, E.: La cnmc multa a endesa y naturgy con 25 millones por alterar los precios de la electricidad (2019)
Segal, M.R.: Machine learning benchmarks and random forest regression (2004)
Shen, G., Tan, Q., Zhang, H., Zeng, P., Xu, J.: Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Comput. Sci. 131, 895–903 (2018). https://doi.org/10.1016/j.procs.2018.04.298. https://www.sciencedirect.com/science/article/pii/S1877050918306781, recent Advancement in Information and Communication Technology
Torgo, L.: Functional models for regression tree leaves. In: ICML, vol. 97, pp. 385–393. Citeseer (1997)
Torres, J.F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., Troncoso, A.: Deep learning for time series forecasting: a survey. Big Data 9(1), 3–21 (2021)
Toubeau, J.F., Bottieau, J., Vallée, F., De Grève, Z.: Deep learning-based multivariate probabilistic forecasting for short-term scheduling in power markets. IEEE Trans. Power Syst. 34(2), 1203–1215 (2019). https://doi.org/10.1109/TPWRS.2018.2870041
Vega, B., Solís, J.: Multivariate time series forecasting with deep learning (2021). https://github.com/javiersgjavi/electric-multivariate
Wang, F., et al.: Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting. Int. J. Electr. Power Energy Syst. 105, 529–540 (2019)
Zhang, J., Tan, Z., Wei, Y.: An adaptive hybrid model for short term electricity price forecasting. Appl. Energy 258, 114087 (2020)
Zhang, R., Li, G., Ma, Z.: A deep learning based hybrid framework for day-ahead electricity price forecasting. IEEE Access 8, 143423–143436 (2020)
Zhang, X., Wang, J., Gao, Y.: A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and svm. Energy Econ. 81, 899–913 (2019)
Zhou, S., Zhou, L., Mao, M., Tai, H.M., Wan, Y.: An optimized heterogeneous structure lstm network for electricity price forecasting. IEEE Access 7, 108161–108173 (2019)
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Vega-Márquez, B., Solís-García, J., Nepomuceno-Chamorro, I.A., Rubio-Escudero, C. (2022). An Extensive Comparative Between Univariate and Multivariate Deep Learning Models in Day-Ahead Electricity Price Forecasting. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_64
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