Yibin Zhao
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[1] H. Wang, N. Zhang, E. Du, J. Yan, S. Han, and Y. Liu, “A com-prehensive review for wind, solar, and electrical load forecastingmethods,” Global Energy Interconnection, vol. 5, pp. 9–30, 2022. [2] A. K. Saha, S. Chowdhury, S. Chowdhury, and A. Domi-jan, “Adaptive network-based fuzzy inference system short-termload forecasting,” International Journal of Power and EnergySystems, vol. 31, no. 3, p. 154, 2011. [3] J. Luo, T. Hong, Z. Gao, and S. C. Fang, “A robust support vec-tor regression model for electric load forecasting,” InternationalJournal of Forecasting, vol. 39, pp. 1005–1020, 2023.15 [4] J. Bi, X. Zhang, H. Yuan, J. Zhang, and M. Zhou, “A hybrid pre-diction method for realistic network traffic with temporal convo-lutional network and lstm,” IEEE Transactions on AutomationScience and Engineering, vol. 19, pp. 1869–1879, 2021. [5] J. Dong, Y. Jiang, P. Chen, J. Li, Z. Wang, and S. Han, “Short-term power load forecasting using bidirectional gated recurrentunits-based adaptive stacked autoencoder,” International Jour-nal of Electrical Power & Energy Systems, vol. 165, p. 110459,2025. [6] V. V. Veeramsetty, K. Raghu Reddy, M. Santhosh, A. Mohnot,and G. Singal, “Short-term electric power load forecasting usingrandom forest and gated recurrent unit,” Electrical Engineering,vol. 104, pp. 307–329, 2022. [7] V. Y. Kondaiah, B. Saravanan, P. Sanjeevikumar, and B. Khan,“A review on short-term load forecasting models for micro-gridapplication,” The Journal of Engineering, pp. 665–689, 2022. [8] X.-B. Jin, W.-Z. Zheng, J.-L. Kong, X.-Y. Wang, Y.-T. Bai,T.-L. Su, et al., “Deep-learning forecasting method for electricpower load via attention-based encoder-decoder with bayesianoptimization,” Energies, vol. 14, p. 1596, 2021. [9] C.-S. Lai, Y. Yang, K. Pan, J. Zhang, H. Yuan, W.-W. Ng,et al., “Multi-view neural network ensemble for short and mid-term load forecasting,” IEEE Transactions on Power Systems,vol. 36, pp. 2992–3003, 2020. [10] D. Obst, J. De Vilmarest, and Y. Goude, “Adaptive methodsfor short-term electricity load forecasting during covid-19 lock-down in france,” IEEE Transactions on Power Systems, vol. 36,pp. 4754–4763, 2021. [11] J. Yan, L. Hu, Z. Zhen, F. Wang, G. Qiu, Y. Li, et al.,“Frequency-domain decomposition and deep learning based so-lar pv power ultra-short-term forecasting model,” IEEE Trans-actions on Industry Applications, vol. 57, pp. 3282–3295, 2021. [12] B. Wang, M. Mazhari, and C. Y. Chung, “A novel hybridmethod for short-term probabilistic load forecasting in distri-bution networks,” IEEE Transactions on Smart Grid, vol. 13,no. 5, pp. 3650–3661, 2022. [13] S. H. Ryu and Y. Yu, “Quantile-mixer: A novel deep learningapproach for probabilistic short-term load forecasting,” IEEETransactions on Smart Grid, vol. 15, pp. 2237–2250, 2023. [14] X. Dong, S. Deng, and D. Wang, “A short-term power load fore-casting method based on k-means and svm,” Journal of AmbientIntelligence and Humanized Computing, vol. 13, pp. 5253–5267,2022. [15] J. Wu, X. Tang, D. Zhou, W. Deng, and Q. Cai, “Applicationof improved dbn and gru based on intelligent optimization al-gorithm in power load identification and prediction,” EnergyInformatics, vol. 7, p. 36, 2024. [16] X. Tang, D. Xiong, Y. Zhang, W. Jiang, and M. Zhou, “Short-term power load forecasting based on extreme gradient boostingand temporal convolutional network,” High Voltage Engineer-ing, vol. 48, pp. 3059–3067, 2022. [17] Z. Xu, Z. Yu, H. Zhang, J. Chen, J. Gu, T. Lukasiewicz, et al.,“Phacia-tcns: Short-term load forecasting using temporal con-volutional networks with parallel hybrid activated convolutionand input attention,” IEEE Transactions on Network Scienceand Engineering, vol. 11, pp. 427–438, 2023. [18] C. Fan, C. Ding, L. Xiao, F. Cheng, and Z. Ai, “Deep beliefensemble network based on moea/d for short-term load fore-casting,” Nonlinear Dynamics, vol. 105, pp. 2405–2430, 2021. [19] Q. Lyu, L. Sun, Y. Che, and Q. Yu, “Load forecasting of dis-tribution network based on deep belief networks,” ShandongElectric Power, vol. 50, pp. 20–26, 2023. [20] G. Wang and J. Leng, “Short-term power load forecasting basedon esmd-pe and adbn,” Electrical Measurement & Instrumen-tation, vol. 60, pp. 29–35, 2023. [21] J. Mo, R. Wang, M. Cao, K. Yang, X. Yang, and T. Zhang, “Ahybrid temporal convolutional network and prophet model forpower load forecasting,” Complex & Intelligent Systems, vol. 9,pp. 4249–4261, 2023. [22] C. Tong, L. Zhang, H. Li, and Y. Ding, “Temporal inceptionconvolutional network based on multi-head attention for ultra-short-term load forecasting,” IET Generation, Transmission &Distribution, vol. 16, pp. 1680–1696, 2022. [23] S. Hajari, A. Yadav, N. Singh, and V. Mahajan, “Impact ofpv, wt, gtg, and ess on the reliability of distribution system,”Majlesi Journal of Electrical Engineering, vol. 17, no. 2, 2023. [24] A. K. Yadav, S. Mudgal, and V. Mahajan, “Reliability test ofrestructured power system with capacity expansion and trans-mission switching,” in 2019 8th International Conference onPower Systems (ICPS), pp. 1–6, IEEE, 2019. [25] A. K. Yadav and V. Mahajan, “Tie-line modelling in intercon-nected synchrophasor network for monitoring grid observability,cyber intrusion and reliability,” Engineering Review, vol. 42,no. 2, pp. 114–132, 2022. [26] A. K. Y. V. Mahajan, “Cyber-attack and reliability monitoringof the synchrophasor smart grid network,” Jurnal Kejuruteraan,vol. 34, no. 6, pp. 1149–1168, 2022. [27] M. C. Ruiz-Abell´on, L. A. Fern´andez-Jim´enez, A. Guillam´on,and A. Gabald´on, “Applications of probabilistic forecasting indemand response,” Applied Sciences, vol. 14, no. 21, p. 9716,2024. [28] B. Shafie and H. Zareipour, “Long-term multi-resolution prob-abilistic load forecasting using temporal hierarchies,” Energies,vol. 18, no. 11, p. 2908, 2025. [29] W. Feng, B. Deng, T. Chen, Z. Zhang, Y. Fu, Y. Zheng, andZ. Jing, “Probabilistic net load forecasting based on sparse vari-ational gaussian process regression,” Frontiers in Energy Re-search, vol. 12, p. 1429241, 2024. [30] V. Y. Singarao and V. S. Rao, “Economic analysis of reservemanagement strategies for grid-connected wind farms,” Inter-national Journal of Power and Energy Systems, vol. 35, no. 3,2015. [31] A. A. Tiguercha, A. Ladjici, and M. Boudour, “Deregulated elec-tricity market calculation based on neuroevolution algorithm,”International Journal of Power and Energy Systems, vol. 38,no. 1, pp. 1–22, 2018. [32] B. Feng, Z. Ding, and C. Jiang, “Fast: A forecasting model withadaptive sliding window and time locality integration for dy-namic cloud workloads,” IEEE Transactions on Services Com-puting, vol. 16, pp. 1184–1197, 2022. [33] K. Dorji, S. Jittanon, P. Thanarak, P. Mensin, and C. Termrit-thikun, “Electricity load forecasting using hybrid datasets withlinear interpolation and synthetic data,” Engineering, Technol-ogy & Applied Science Research, vol. 14, pp. 17931–17938, 2024. [34] X. Zhang, Z. Qi, M. Gu, W. Miao, Q. Fan, and Z. Ma, “Co-operative edge caching based on temporal convolutional net-works,” IEEE Transactions on Parallel and Distributed Sys-tems, vol. 33, pp. 2093–2105, 2021. [35] N. T. Tran, T. A. Nguyen, and M. B. Lam, “A new grid searchalgorithm based on xgboost model for load forecasting,” Bulletinof Electrical Engineering and Informatics, vol. 12, pp. 1857–1866, 2023. [36] J. Wei, X. Wu, T. Yang, and R. Jiao, “Ultra-short-term fore-casting of wind power based on multi-task learning and lstm,”International Journal of Electrical Power and Energy Systems,vol. 149, p. 109073, 2023.16
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