Short-Term Electricity Price Forecasting Using EEMD and GRU-NN
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DOI:
https://doi.org/10.59287/ijanser.772Keywords:
Machine Learning, GRU-NN, Short-Term Electricity Price Forecasting, EEMD, Feature SelectionAbstract
A vital necessity for the reliable operation of the power system as well as for the financial well-being of the consumers is an accurate forecast of the price of electricity. The price of electricity is highly volatile, nonlinear, and subject to seasonal fluctuations. The price series is also affected by electrical market uncertainty and demand, which is strongly reliant on weather and electricity usage time. This study presents a short-term electricity price forecasting method that follows the similar days approach comprising Ensemble Empirical Mode Decomposition technique. The suggested method utilizes a feature selection methodology that combines the Random Forest Regressor and the Gradient Boosting Regressor to determine which features are most important for the machine learning model. To forecast the electricity price, a Gated Recurrent Unit Neural Network (GRU-NN) is employed as the machine learning model. The GRU-NN has the capability of accurately capturing complicated temporal relationships in electricity price time series, which enables it to make correct predictions. To evaluate the validity of the proposed method, data from the PJM electricity market have been used. The simulation results demonstrate that the suggested method is superior to the existing technique, with significantly improved values achieved for both the mean absolute percentage error (MAPE) and the root mean square error (RMSE).
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References
Z. Yang, L. Ce, and L. Lian, “Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods,” Appl. Energy, vol. 190, no. C, pp. 291–305, 2017, doi: 10.1016/j.apenergy.2016.1.
S. Aslam, H. Herodotou, S. M. Mohsin, N. Javaid, N. Ashraf, and S. Aslam, “A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids,” Renew. Sustain. Energy Rev., vol. 144, no. December 2020, p. 110992, 2021, doi: 10.1016/j.rser.2021.110992.
A. J. Conejo, M. A. Plazas, R. Espinola, and A. B. Molina, “Day-ahead electricity price forecasting using the wavelet transform and ARIMA models,” IEEE Trans. Power Syst., vol. 20, no. 2, pp. 1035–1042, 2005, doi: 10.1109/TPWRS.2005.846054.
C. P. Rodriguez and G. J. Anders, “Energy price forecasting in the Ontario competitive power system market,” IEEE Trans. Power Syst., vol. 19, no. 1, pp. 366–374, 2004, doi: 10.1109/TPWRS.2003.821470.
H. Y. Yamin, S. M. Shahidehpour, and Z. Li, “Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets,” Int. J. Electr. Power Energy Syst., vol. 26, no. 8, pp. 571–581, 2004, doi: https://doi.org/10.1016/j.ijepes.2004.04.005.
A. Weron Rafałand Misiorek, “Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models,” Int. J. Forecast., vol. 24, no. 4, pp. 744–763, 2008.
X. Zhu, L. Li, K. Zhou, X. Zhang, and S. Yang, “A meta-analysis on the price elasticity and income elasticity of residential electricity demand,” J. Clean. Prod., vol. 201, pp. 169–177, 2018.
J. Lago, F. De Ridder, P. Vrancx, and B. De Schutter, “Forecasting day-ahead electricity prices in Europe: The importance of considering market integration,” Appl. Energy, vol. 211, pp. 890–903, 2018.
M. A. Shah, I. A. Sajjad, M. F. N. Khan, M. M. Iqbal, R. Liaqat, and M. Z. Shah, “Short-term Meter Level Load Forecasting of Residential Customers Based on Smart Meter’s Data,” in 2020 International Conference on Engineering and Emerging Technologies (ICEET), 2020, pp. 1–6, doi: 10.1109/ICEET48479.2020.9048196.
W. Qiao and Z. Yang, “Forecast the electricity price of U.S. using a wavelet transform-based hybrid model,” Energy, vol. 193, p. 116704, 2020, doi: 10.1016/j.energy.2019.116704.
J. P. González, A. M. S. Muñoz San Roque, and E. A. Pérez, “Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting,” IEEE Trans. Power Syst., vol. 33, no. 1, pp. 545–556, 2018, doi: 10.1109/TPWRS.2017.2700287.
R. Bisoi, P. K. Dash, and P. P. Das, “Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine,” Neural Comput. Appl., vol. 32, no. 5, pp. 1457–1480, 2020, doi: 10.1007/s00521-018-3652-5.
S. Albahli, M. Shiraz, and N. Ayub, “Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model,” IEEE Access, vol. 8, pp. 200971–200981, 2020, doi: 10.1109/ACCESS.2020.3035328.
T. Van Der Heijden, J. Lago, P. Palensky, and E. Abraham, “Electricity Price Forecasting in European Day Ahead Markets: A Greedy Consideration of Market Integration,” IEEE Access, vol. 9, pp. 119954–119966, 2021, doi: 10.1109/ACCESS.2021.3108629.
Y. Y. Hong, J. V. Taylar, and A. C. Fajardo, “Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm,” IEEE Access, vol. 8, pp. 91975–91988, 2020, doi: 10.1109/ACCESS.2020.2994444.
C. Y. Lee and C. E. Wu, “Short-term electricity price forecasting based on similar day-based neural network,” Energies, vol. 13, no. 17, 2020, doi: 10.3390/en13174408.
P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, and A. K. Srivastava, “A novel approach to forecast electricity price for PJM using neural network and similar days method,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 2058–2065, 2007, doi: 10.1109/TPWRS.2007.907386.
E. Yiğit, U. Özkaya, Ş. Öztürk, D. Singh & H. Gritli "Automatic detection of power quality disturbance using convolutional neural network structure with gated recurrent unit. Mobile" Information Systems, 2021, 1-11.
R. Khalid, N. Javaid, F. A. Al-zahrani, K. Aurangzeb, E. U. H. Qazi, and T. Ashfaq, “Electricity load and price forecasting using jaya-long short term memory (JLSTM) in smart grids,” Entropy, vol. 22, no. 1, p. 10, 2020, doi: 10.3390/e22010010.
R. Zhang, G. Li, and Z. Ma, “A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting,” IEEE Access, vol. 8, pp. 143423–143436, 2020, doi: 10.1109/ACCESS.2020.3014241.
S. Khan, S. Aslam, I. Mustafa, and S. Aslam, “Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine,” Forecasting, vol. 3, no. 3, pp. 460–477, 2021, doi: 10.3390/forecast3030028.
N. E. H. Zhaohua Wu, “Ensemble empirical mode decomposition: A Noise-Assited,” Biomed. Tech., vol. 55, no. 1, pp. 193–201, 2010.
V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, and B. P. Feuston, “Random forest: a classification and regression tool for compound classification and QSAR modeling.,” J. Chem. Inf. Comput. Sci., vol. 43, no. 6, pp. 1947–1958, 2003, doi: 10.1021/ci034160g.
S. Karasu and A. Altan, “Recognition Model for Solar Radiation Time Series based on Random Forest with Feature Selection Approach,” in 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), 2019, pp. 8–11, doi: 10.23919/ELECO47770.2019.8990664.
D. Upadhyay, J. Manero, M. Zaman, and S. Sampalli, “Learning Classifiers for Intrusion Detection on Power Grids,” Ieee Trans. Netw. Serv. Manag., vol. 18, no. 1, pp. 1104–1116, 2021.
Z. Xu, G. Huang, K. Q. Weinberger, and A. X. Zheng, “Gradient boosted feature selection,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., no. August, pp. 522–531, 2014, doi: 10.1145/2623330.2623635.
J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” 2001.
R. Yu et al., “LSTM-EFG for wind power forecasting based on sequential correlation features,” Futur. Gener. Comput. Syst., vol. 93, pp. 33–42, 2019, doi: https://doi.org/10.1016/j.future.2018.09.054.
S. Hochreiter and J. Schmidhuber, “Long short-term memory.,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio, “On the Properties of Neural Machine Translation: Encoder{--}Decoder Approaches,” in Proceedings of {SSST}-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Oct. 2014, pp. 103–111, doi: 10.3115/v1/W14-4012.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” no. December, 2014, [Online]. Available: http://arxiv.org/abs/1412.3555.
M. Jiang, W. Chen, and X. Li, “S-GCN-GRU-NN: A novel hybrid model by combining a Spatiotemporal Graph Convolutional Network and a Gated Recurrent Units Neural Network for short-term traffic speed forecasting,” J. Data, Inf. Manag., vol. 3, no. 1, pp. 1–20, 2021, doi: 10.1007/s42488-020-00037-9.
G. Memarzadeh and F. Keynia, “Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm,” Electr. Power Syst. Res., vol. 192, no. December 2020, p. 106995, 2021, doi: 10.1016/j.epsr.2020.106995.
“Google: What is Colaboratory? Available online.” https://colab.research.google.com/.
