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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. 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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] |
