APPLYING SEQ2SEQ-LSTM IN A SHORT-TERM LOAD FORECASTING MODEL FOR THE POWER GRID IN TIEN GIANG

Dương Ngọc Hùng; Nguyễn Minh Tâm; Nguyễn Tùng Linh; Nguyễn Thanh Hoan; Nguyễn Thanh Duy

doi:10.34238/tnu-jst.9060

APPLYING SEQ2SEQ-LSTM IN A SHORT-TERM LOAD FORECASTING MODEL FOR THE POWER GRID IN TIEN GIANG

About this article

Received: 26/10/23 Revised: 27/11/23 Published: 27/11/23

Authors

1. Duong Ngoc Hung, 1) HCM University of Technology and Education, 2) Tien Giang University
2. Nguyen Minh Tam, HCM University of Technology and Education
3. Nguyen Tung Linh , Electric Power University
4. Nguyen Thanh Hoan, Department of Information Technology - Ho Chi Minh Power Corporation (EVNHCMC)
5. Nguyen Thanh Duy, Tien Giang University

Abstract

Short-term load forecasting is critical for energy suppliers to meet the loads of consumers connected to the grid. This study explores the performance of short-term load demand forecasting models, including CNN-LSTM, Wavenet, and Seq2Seq integrating long short-term memory (LSTM). The Seq2Seq-LSTM prediction model is established by combining the sequence-to-sequence (Seq2Seq) structure with the long-short-term neuron model to improve the prediction accuracy. The study validates the models using demand data from the Tien Giang power system from 2021 to 2022, taking into consideration historical demand, holidays and weather variables as input characteristics. The results show that both CNN-LSTM, Wavenet and Seq2Seq-LSTM models can predict future demand with root mean square error (RMSE) and mean absolute percentage error (MAPE). Therefore, the proposed models are not only a useful tool in making smart decisions and planning for future energy needs, but can also play an important role in minimizing Optimize the use of energy resources, minimize negative environmental impacts, and improve the overall performance of the energy sector.

Keywords

Wavenet; LSTM; CNN; Seq2Seq; Peak load forecasting

Full Text:

PDF (Tiếng Việt)

References

[1] Y. Ghadi, N. Ahmad, M. Adnan, and M. Ali, “Load Forecasting Techniques for Power System: Research Challenges and Survey,” IEEE Access, vol. 10, pp. 71054-71090, 2022.

[2] M. Fan, X. Zhang, H. Yin, Y. Hu, Q. Yang, and L. Fan, “Short-term Load Forecasting for Distribution Network Using Decomposition with Ensemble prediction," 2019 Chinese Automation Congress (CAC), Hangzhou, China, 2019, pp. 152-157.

[3] J. Zhu, H. Dong, W. Zheng, Y. Huang, S. Li, and L. Xi, “Review and prospect of data-driven techniques for load forecasting in integrated energy systems,” Applied Energy, vol. 321, 2022, Art. no. 119269.

[4] F. D. Rueda and J. D. Suárez, “Alejandro del Real Torres. Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid,” Energies, vol. 14, 2021, Art. no. 2524, doi: 1996-1073/14/9/2524.

[5] D. Niu, Y. Wu, and D. D. Wang, “Power load forecasting using support vector machine and ant colony optimization,” Expert Syst. Appl., vol. 37, pp. 2531–2539, 2010.

[6] Y. -H. Guo, J. -M. Zheng, G. –F. Fan, and W.-C. Hong, “Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting,” Energies, vol. 12, 2019, Art. no. 916.

[7] Y. Dong, W.-C Hong, and Z. Zhang, “A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting,” Energies, vol. 11, 2018, Art. no. 1009.

[8] R. Zhang, Y. Xu, Z. Y. Dong, K. Meng, and K. P. Wong, “Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine,” IET Gener. Transm. Distrib, vol. 7, pp. 391–397, 2013.

[9] M. Ghofrani, A. Ghayekhloo, M. Ghayekhloo, and A. Arabali, “A hybrid short-term load forecasting with a new input selection framework,” Energy, vol. 81, pp. 777–786, 2015.

[10] W. Kong, Y. Jia, D. J. Hill, Z. Y. Dong, Y. Xu, and Y. Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network,” IEEE Trans Smart Grid, vol. 10, pp. 841–851, 2017.

[11] C. Tian, C. Zhang, J. Ma, and P. Zhan, “A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network,” Energies, vol. 11, 2018, Art. no. 3493.

[12] L. Han, Y. Li, B. Yong, Y. Peng, Q. Zhou, and L. Shu, “Enhanced deep networks for short-term and medium-term load forecasting,” IEEE Access, vol. 7, pp. 4045–4055, 2018.

[13] K. Park, E. Hwang, and S. Yoon, “Hybrid load forecasting for mixed-use complex based on the characteristic load decomposition by pilot signals,” IEEE Access, vol. 7, pp. 12297–12306, 2019.

[14] C. Ming-Wei, C. Bo-Juen, L. Chih-Jen, “Load forecasting using support vector Machines: a study on EUNITE competition 2001,” IEEE Trans Power Syst., vol. 19, no. 4, 2004, Art. no. 1821e30.

[15] J. Che and J. Wang “Short-term load forecasting using a kernel-based support vector regression combination model,” Appl. Energy, vol. 132, 2014, Art. no. 602e9.

[16] W.-C. Hong “Electric load forecasting by support vector model,” Appl. Math Model, vol. 33, no. 5, 2009, Art. no. 2444e54.

[17] E. Ceperic, A. Baric, and V. Ceperic “A strategy for short-term load forecasting by support vector regression machines,” IEEE Trans Power Syst, vol. 28, no. 4, 2013, Art. no. 4356e64.

[18] A. Selakov, L. Milovic, S. Mellon, D. Cvijetinovic, and D. Bekut, “Hybrid PSOeSVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank,” Appl. Soft Comput., vol. 16, 2014, Art. no. 80e8.

[19] W. Sun, “A novel hybrid GA based SVM short term load forecasting model,” The Second International Symposium on Knowledge Acquisition and Modeling, vol. 2, 2009, Art. no. 227e9.

[20] A. Kavousi-Fard, F. Marzbani, and H. Samet, “A new hybrid modified firefly algorithm and support vector regression model for accurate short-term load forecasting,” Expert Syst. Appl., vol. 41, no. 13, 2014, Art. no. 6047e56.

[21] M. Barman and N. B. D. Choudhury, “Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept,” Energy, vol. 174, 2019, Art. no. 886e96.

[22] M. Barman, S. Sutradhar, and N. B. D. Choudhury, “A regional hybrid Goa-SVM model based on similar day approach for short-term load forecasting in Assam, India,” Energy, vol. 145, 2018, Art. no. 710e20.

[23] H. Lu, T. Iseley, and M. Azimi, “Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine,” Energy Rep., vol. 5, 2019, Art. no. 666e77.

[24] Y. Zhang, E. Muljadi, H. Jiang, J. J. Zhang, and D. W. Gao “A short-term and highresolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization,” IEEE Transactions on Smart Grid, vol. 9, no. 4, 2018, Art. no. 3341e50.

[25] X. Wang and Y. Wang “A hybrid model of EMD and PSO-SVR for short-term load forecasting in residential quarters,” Journal of Mathematical Problems in Engineering, vol. 2016, 2016, Art. no. 9895639.

[26] Q. Chen, X. Zhang, Y. Wu, and X. Chen, “Forecasting system based on wavelet transform and PSO-SVM. 2nd international conference on anti-counterfeiting,” 2nd International Conference on Anti-counterfeiting, Security and Identification, 2008, pp. 305-309.

[27] S. Qiang and Y. Pu “Short-term power load forecasting based on support vector machine and particle swarm optimization,” J. Algorithm Comput. Technol., vol. 13, pp.1-8, 2018.

[28] M. Ye and W. Sun, “Short-term load forecasting based on wavelet transform and least squares support vector machine optimized by fruit fly optimization algorithm,” Journal of Electrical and Computer Engineering, vol. 2015, 2015, Art. no. 862185.

[29] J.–P. Liu and C.-L. Li, “The short-term power load forecasting based on sperm whale algorithm and wavelet least square support vector machine with DWT-IR for feature selection,” Sustainability, vol. 9, no. 7, 2017, Art. no. 1188.

[30] L. Peng, S.-X. Lv, L. Wang, and Z. -Y. Wang, “Effective electricity load forecasting using enhanced double-reservoir echo state network,” Eng. Appl. Artif. Intell., vol. 99, 2021, Art. no. 104132, doi: 10.1016/j.engappai.2020.104132.

[31] V. K. Rayi, S. Mishra, J. Naik, and P. Dash, “Adaptive vmd based optimized deep learning mixed kernel elm autoencoder for single and multistep wind power forecasting,” Energy, vol. 244, 2022, Art. no. 122585, doi: 10.1016/j.energy.2021.122585.

[32] Y. Dong, Z. Dong, T. Zhao, Z. Li, and Z. Ding, “Short term load forecasting with markovian switching distributed deep belief networks,” Int. J. Electr. Power & Energy Syst., vol. 130, 2021, Art. no. 106942. doi: 10.1016/j.ijepes.2021.106942.

[33] T. Bashir, C. Haoyong, M. F. Tahir, and Z. Liqiang, “Short term electricity load forecasting using hybrid prophet-lstm model optimized by bpnn,” Energy Rep., vol. 8, 2022, pp. 1678–1686, doi: 10.1016/j.egyr.2021.12.067

[34] I. S. Jahan, V. Snasel, and S. Misak, “Intelligent systems for power load forecasting: A study review,” Energies, vol. 13, 2020, Art. no. 6105, doi:10.3390/en13226105.

DOI: https://doi.org/10.34238/tnu-jst.9060

Refbacks

There are currently no refbacks.



Remember me