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Zheng, "Shortterm Electric Load Forecasting Based on Wavelet Neural Network, Particle Swarm Optimization and Ensemble Empirical Mode Decomposition," Energy Procedia, vol. 105, pp. 36773682, 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. 653663, 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. 9899299008, 2021. [ DOI:10.1109/ACCESS.2021.3095420] 53. W. Dong, H. Sun, J. Tan, Z. Li, J. Zhang, and Y. Y. Zhao, "Shortterm regional wind power forecasting for small datasets with input data correction, hybrid neural network, and error analysis," Energy Reports, vol. 7, pp. 76757692, 2021/11/01/ 2021. 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Afrasiabi, and M. Mohammadi, "Deep Learning Forecaster based Controller for SVC: Wind Farm Flicker Mitigation," IEEE Transactions on Industrial Informatics, pp. 11, 2020. 58. X. Kong, C. Li, F. Zheng, and C. Wang, "Improved Deep Belief Network for ShortTerm Load Forecasting Considering DemandSide Management," IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 15311538, 2020. [ DOI:10.1109/TPWRS.2019.2943972] 59. H. Chen, S. Wang, S. Wang, and Y. Li, "Dayahead aggregated load forecasting based on twoterminal 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, "TwoStage Deep Learningbased Wind Turbine Condition Monitoring Using SCADA Data," in 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020, pp. 16. [ DOI:10.1109/PEDES49360.2020.9379393] 61. S. Afrasiabi, M. Afrasiabi, B. Parang, M. Mohammadi, H. Samet, and T. Dragicevic, "Fast GRNNBased Method for Distinguishing Inrush Currents in Power Transformers," IEEE Transactions on Industrial Electronics, pp. 11, 2021. [ DOI:10.1109/TIE.2021.3109535] 62. S. Afrasiabi et al., "Detection and Localization of Transmission Line Faults based on a Hybrid TwoStage 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. 15. [ 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. 22612269. [ 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. 13031314, 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. 865876, 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. 720727, 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. 111, 2022. [ DOI:10.1109/JSYST.2022.3172406]
