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:: Volume 12, Issue 2 (8-2023) ::
ieijqp 2023, 12(2): 1-11 Back to browse issues page
Design of a multi-level deep residual neural network for short-term prediction of electrical loads in power systems
Mahtab Ganjouri , Mazda Moattari *1 , Ahmad Forouzantabar , Mohammad Azadi
Abstract:   (1542 Views)

To maintain supply-demand balance, it is crucial to design a method to provide prior knowledge on load consumption in look-ahead time with high level of accuracy and reliability. The load prediction problem is becoming more and more challenging due to emerging new concepts in the electrical grids and reconstruction of the power networks. This paper develops a residual neural network to predict the electrical loads with high level of accuracy. In the designed network with combining two powerful deep residual network, a new residual deep network is proposed to improve the learning ability as well as prevent problems like overfitting and gradient reduction/explosion. Furthermore, to fully understand the spatial-temporal features, convolutional neural network (CNN) and gated recurrent unit (GRU) are combined and integrated into the designed multi-level deep network. The seasonal analysis and investigating several cases using actual electrical load consumption in Shiraz, Iran verifies the effectiveness of the proposed method and higher accuracy of the proposed deep network in comparison with other methods demonstrate the superiority of the proposed method.
Article number: 1
Keywords: Short-term load forecasting, multi-level residual deep neural network, gated recurrent network, convolutional neural network
Full-Text [PDF 1087 kb]   (300 Downloads)    
Type of Study: Research |
Received: 2022/07/11 | Accepted: 2023/06/27 | Published: 2023/08/1
References
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5. M. Afrasiabi, M. Mohammadi, M. Rastegar, L. Stankovic, S. Afrasiabi, and M. Khazaei, "Deep-Based Conditional Probability Density Function Forecasting of Residential Loads," IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3646-3657, 2020. [DOI:10.1109/TSG.2020.2972513]
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18. R. K. Jagait, M. N. Fekri, K. Grolinger, and S. Mir, "Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA," IEEE Access, vol. 9, pp. 98992-99008, 2021. [DOI:10.1109/ACCESS.2021.3095420]
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20. S. Afrasiabi, M. Mohammadi, M. Afrasiabi, and B. Parang, "Modulated Gabor filter based deep convolutional network for electrical motor bearing fault classification and diagnosis," IET Science, Measurement & Technology, vol. 15, no. 2, pp. 154-162, 2021. [DOI:10.1049/smt2.12017]
21. S. Afrasiabi, M. Afrasiabi, M. Mohammadi, and B. Parang, "Fault localisation and diagnosis in transmission networks based on robust deep Gabor convolutional neural network and PMU measurements," IET Generation, Transmission & Distribution, vol. 14, no. 26, pp. 6484- [DOI:10.1049/iet-gtd.2020.0856]
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23. H. Samet, S. Ketabipoor, M. Afrasiabi, S. Afrasiabi, and M. Mohammadi, "Deep Learning Forecaster based Controller for SVC: Wind Farm Flicker Mitigation," IEEE Transactions on Industrial Informatics, pp. 1-1, 2020.
24. X. Kong, C. Li, F. Zheng, and C. Wang, "Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management," IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1531-1538, 2020. [DOI:10.1109/TPWRS.2019.2943972]
25. H. Chen, S. Wang, S. Wang, and Y. Li, "Day-ahead aggregated load forecasting based on two-terminal sparse coding and deep neural network fusion," Electric Power Systems Research, vol. 177, p. 105987, 2019/12/01/ 2019. [DOI:10.1016/j.epsr.2019.105987]
26. S. Afrasiabi, M. Afrasiabi, B. Parang, M. Mohammadi, S. Kahourzade, and A. Mahmoudi, "Two-Stage Deep Learning-based Wind Turbine Condition Monitoring Using SCADA Data," in 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020, pp. 1-6. [DOI:10.1109/PEDES49360.2020.9379393]
27. S. Afrasiabi, M. Afrasiabi, B. Parang, M. Mohammadi, H. Samet, and T. Dragicevic, "Fast GRNN-Based Method for Distinguishing Inrush Currents in Power Transformers," IEEE Transactions on Industrial Electronics, pp. 1-1, 2021. [DOI:10.1109/TIE.2021.3109535]
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32. M. Afrasiabi, M. Mohammadi, M. Rastegar, and S. Afrasiabi, "Advanced Deep Learning Approach for Probabilistic Wind Speed Forecasting," IEEE Transactions on Industrial Informatics, vol. 17, no. 1, pp. 720-727, 2021. [DOI:10.1109/TII.2020.3004436]
33. S. Afrasiabi, M. Afrasiabi, M. A. Jarrahi, and M. Mohammadi, "Fault Location and Faulty Line Selection in Transmission Networks: Application of Improved Gated Recurrent Unit," IEEE Systems Journal, pp. 1-11, 2022. [DOI:10.1109/JSYST.2022.3172406]
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36. M. Afrasiabi, M. Mohammadi, M. Rastegar, and S. Afrasiabi, "Stochastic distributed microgrid energy management based on over-relaxed alternative direction method of multipliers," IET Renewable Power Generation, vol. 14, no. 14, pp. 2639-2648, 2020. [DOI:10.1049/iet-rpg.2019.1395]
37. H. Samet, S. Ketabipour, S. Afrasiabi, M. Afrasiabi, and M. Mohammadi, "Prediction of wind farm reactive power fast variations by adaptive one-dimensional convolutional neural network," Computers & Electrical Engineering, vol. 96, p. 107480, 2021/12/01/ 2021. [DOI:10.1016/j.compeleceng.2021.107480]
38. M. Afrasiabi, M. Mohammadi, M. Rastegar, and A. Kargarian, "Probabilistic deep neural network price forecasting based on residential load and wind speed predictions," IET Renewable Power Generation, vol. 13, no. 11, pp. 1840-1848, 2019. [DOI:10.1049/iet-rpg.2018.6257]
39. M. Afrasiabi, M. Mohammadi, M. Rastegar, L. Stankovic, S. Afrasiabi, and M. Khazaei, "Deep-Based Conditional Probability Density Function Forecasting of Residential Loads," IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3646-3657, 2020. [DOI:10.1109/TSG.2020.2972513]
40. L. Jian-Chang, N. Dong-Xiao, and J. Zheng-Yuan, "A study of short-term load forecasting based on ARIMA-ANN," in Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2004, vol. 5, pp. 3183-3187 vol.5.
41. H. Chen, Q. Wan, B. Zhang, F. Li, and Y. Wang, "Short-term load forecasting based on asymmetric ARCH models," in IEEE PES General Meeting, 2010, pp. 1-6. [DOI:10.1109/PES.2010.5590185]
42. H. Cui and X. Peng, "Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model," Mathematical Problems in Engineering, vol. 2015, p. 589374, 2015/07/14 2015. [DOI:10.1155/2015/589374]
43. S. Afrasiabi, M. Afrasiabi, B. Parang, and M. Mohammadi, "Designing a composite deep learning based differential protection scheme of power transformers," Applied Soft Computing, vol. 87, p. 105975, 2020/02/01/ 2020. [DOI:10.1016/j.asoc.2019.105975]
44. M. Afrasiabi, S. Afrasiabi, B. Parang, and M. Mohammadi, "Power transformers internal fault diagnosis based on deep convolutional neural networks," Journal of Intelligent & Fuzzy Systems, vol. 37, pp. 1165-1179, 2019. [DOI:10.3233/JIFS-182615]
45. C. Hong, C. A. Canizares, and A. Singh, "ANN-based short-term load forecasting in electricity markets," in 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194), 2001, vol. 2, pp. 411-415 vol.2.
46. A. Selakov, D. Cvijetinović, L. Milović, S. Mellon, and D. Bekut, "Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank," Applied Soft Computing, vol. 16, pp. 80-88, 2014/03/01/ 2014. [DOI:10.1016/j.asoc.2013.12.001]
47. P. Zhang, X. Wu, X. Wang, and S. Bi, "Short-term load forecasting based on big data technologies," CSEE Journal of Power and Energy Systems, vol. 1, no. 3, pp. 59-67, 2015. [DOI:10.17775/CSEEJPES.2015.00036]
48. C. Ying-Ying, P. P. K. Chan, and Q. Zhi-Wei, "Random forest based ensemble system for short term load forecasting," in 2012 International Conference on Machine Learning and Cybernetics, 2012, vol. 1, pp. 52-56.
49. M. El-Hendawi and Z. Wang, "An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting," Electric Power Systems Research, vol. 182, p. 106265, 2020/05/01/ 2020. [DOI:10.1016/j.epsr.2020.106265]
50. C. López, W. Zhong, and M. Zheng, "Short-term Electric Load Forecasting Based on Wavelet Neural Network, Particle Swarm Optimization and Ensemble Empirical Mode Decomposition," Energy Procedia, vol. 105, pp. 3677-3682, 2017/05/01/ 2017. [DOI:10.1016/j.egypro.2017.03.847]
51. Y. Liang, D. Niu, and W.-C. Hong, "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, vol. 166, pp. 653-663, 2019/01/01/ 2019. [DOI:10.1016/j.energy.2018.10.119]
52. R. K. Jagait, M. N. Fekri, K. Grolinger, and S. Mir, "Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA," IEEE Access, vol. 9, pp. 98992-99008, 2021. [DOI:10.1109/ACCESS.2021.3095420]
53. W. Dong, H. Sun, J. Tan, Z. Li, J. Zhang, and Y. Y. Zhao, "Short-term regional wind power forecasting for small datasets with input data correction, hybrid neural network, and error analysis," Energy Reports, vol. 7, pp. 7675-7692, 2021/11/01/ 2021. [DOI:10.1016/j.egyr.2021.11.021]
54. S. Afrasiabi, M. Mohammadi, M. Afrasiabi, and B. Parang, "Modulated Gabor filter based deep convolutional network for electrical motor bearing fault classification and diagnosis," IET Science, Measurement & Technology, vol. 15, no. 2, pp. 154-162, 2021. [DOI:10.1049/smt2.12017]
55. S. Afrasiabi, M. Afrasiabi, M. Mohammadi, and B. Parang, "Fault localisation and diagnosis in transmission networks based on robust deep Gabor convolutional neural network and PMU measurements," IET Generation, Transmission & Distribution, vol. 14, no. 26, pp. 6484- [DOI:10.1049/iet-gtd.2020.0856]
56. S. Afrasiabi, M. Afrasiabi, B. Parang, M. Mohammadi, S. Kahourzade, and A. Mahmoudi, "Two-Stage Deep Learning-based Wind Turbine Condition Monitoring Using SCADA Data," in 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 16-19 Dec. 2020 2020, pp. 1-6. [DOI:10.1109/PEDES49360.2020.9379393]
57. H. Samet, S. Ketabipoor, M. Afrasiabi, S. Afrasiabi, and M. Mohammadi, "Deep Learning Forecaster based Controller for SVC: Wind Farm Flicker Mitigation," IEEE Transactions on Industrial Informatics, pp. 1-1, 2020.
58. X. Kong, C. Li, F. Zheng, and C. Wang, "Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management," IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1531-1538, 2020. [DOI:10.1109/TPWRS.2019.2943972]
59. H. Chen, S. Wang, S. Wang, and Y. Li, "Day-ahead aggregated load forecasting based on two-terminal sparse coding and deep neural network fusion," Electric Power Systems Research, vol. 177, p. 105987, 2019/12/01/ 2019. [DOI:10.1016/j.epsr.2019.105987]
60. S. Afrasiabi, M. Afrasiabi, B. Parang, M. Mohammadi, S. Kahourzade, and A. Mahmoudi, "Two-Stage Deep Learning-based Wind Turbine Condition Monitoring Using SCADA Data," in 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020, pp. 1-6. [DOI:10.1109/PEDES49360.2020.9379393]
61. S. Afrasiabi, M. Afrasiabi, B. Parang, M. Mohammadi, H. Samet, and T. Dragicevic, "Fast GRNN-Based Method for Distinguishing Inrush Currents in Power Transformers," IEEE Transactions on Industrial Electronics, pp. 1-1, 2021. [DOI:10.1109/TIE.2021.3109535]
62. S. Afrasiabi et al., "Detection and Localization of Transmission Line Faults based on a Hybrid Two-Stage Technique considering Wind Power Generation," in 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2021, pp. 1-5. [DOI:10.1109/EEEIC/ICPSEurope51590.2021.9584525]
63. G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261-2269. [DOI:10.1109/CVPR.2017.243]
64. K. Zhang, M. Sun, T. X. Han, X. Yuan, L. Guo, and T. Liu, "Residual Networks of Residual Networks: Multilevel Residual Networks," IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 6, pp. 1303-1314, 2018. [DOI:10.1109/TCSVT.2017.2654543]
65. S. Afrasiabi, M. Afrasiabi, B. Parang, and M. Mohammadi, "Integration of Accelerated Deep Neural Network Into Power Transformer Differential Protection," IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 865-876, 2020. [DOI:10.1109/TII.2019.2929744]
66. M. Afrasiabi, M. Mohammadi, M. Rastegar, and S. Afrasiabi, "Advanced Deep Learning Approach for Probabilistic Wind Speed Forecasting," IEEE Transactions on Industrial Informatics, vol. 17, no. 1, pp. 720-727, 2021. [DOI:10.1109/TII.2020.3004436]
67. S. Afrasiabi, M. Afrasiabi, M. A. Jarrahi, and M. Mohammadi, "Fault Location and Faulty Line Selection in Transmission Networks: Application of Improved Gated Recurrent Unit," IEEE Systems Journal, pp. 1-11, 2022. [DOI:10.1109/JSYST.2022.3172406]

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ganjouri M, Moattari M, Forouzantabar A, Azadi M. Design of a multi-level deep residual neural network for short-term prediction of electrical loads in power systems. ieijqp 2023; 12 (2) : 1
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Volume 12, Issue 2 (8-2023) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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