References:

[1] RB Jackson, P Friedlingstein, RM Andrew, JG Canadell, C Le Que´re´, and GP Peters. Per- sistent fossil fuel growth threatens the paris agreement and planetary health. Environmental Research Letters, 14(12):121001, 2019.
[2] Georgina Santos. Road transport and co2 emissions: What are the challenges? Transport Policy, 59:71–74, 2017.
[3] Roshanak Azarafshar and Wessel N Vermeulen. Electric vehicle incentive policies in cana- dian provinces. Energy Economics, 91:104902, 2020.
[4] Ning Wang, Huizhong Pan, and Wenhui Zheng. Assessment of the incentives on electric vehicle promotion in china. Transportation Research Part A: Policy and Practice, 101:177– 189, 2017.
[5] International Energy Agency. Electric Vehicles, Accessed 2024. Accessed: June 2024.

[6] Francis Mwasilu, Jackson John Justo, Eun-Kyung Kim, Ton Duc Do, and Jin-Woo Jung. Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renewable and sustainable energy reviews, 34:501–516, 2014.
[7] Bharatiraja Chokkalingam, Sanjeevikumar Padmanaban, Pierluigi Siano, Ramesh Krish- namoorthy, and Raghu Selvaraj. Real-time forecasting of ev charging station scheduling for smart energy systems. Energies, 10(3):377, 2017.
[8] Matthew K Gray and Walid G Morsi. Power quality assessment in distribution systems

embedded with plug-in hybrid and battery electric vehicles. IEEE Transactions on Power Systems, 30(2):663–671, 2014.
[9] Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, et al. Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems, 31, 2018.
[10] Gustavo EAPA Batista and Maria Carolina Monard. An analysis of four missing data treat- ment methods for supervised learning. Applied artificial intelligence, 17(5-6):519–533, 2003.
[11] Steffen Moritz and Thomas Bartz-Beielstein. imputets: time series missing value imputation in r. R Journal, 9(1), 2017.
[12] Jinsung Yoon, James Jordon, and Mihaela Schaar. Gain: Missing data imputation using generative adversarial nets. In International conference on machine learning, pages 5689– 5698. PMLR, 2018.
[13] Yonghong Luo, Ying Zhang, Xiangrui Cai, and Xiaojie Yuan. E2gan: End-to-end generative adversarial network for multivariate time series imputation. In Proceedings of the 28th inter- national joint conference on artificial intelligence, pages 3094–3100. AAAI Press Palo Alto, CA, USA, 2019.
[14] Antonio Liguori, Romana Markovic, Thi Thu Ha Dam, Je´roˆme Frisch, Christoph van Treeck, and Francesco Causone. Indoor environment data time-series reconstruction using autoen- coder neural networks. Building and Environment, 191:107623, 2021.
[15] Gary King, James Honaker, Anne Joseph, and Kenneth Scheve. Analyzing incomplete po- litical science data: An alternative algorithm for multiple imputation. American political science review, 95(1):49–69, 2001.
[16] George W. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870–1891, 1992.
[17] J. Zico Kolter and Tommi Jaakkola. Approximate inference in additive factorial hmms with

application to energy disaggregation. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), pages 1472–1482, 2012.
[18] Cheng Zhang, Mingjun Zhong, Zuchao Wang, Neil Goddard, and Charles Sutton. Sequence- to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 2604–2611, 2018.
[19] Roberto Bonfigli, Emanuele Principi, Marco Fagiani, and Stefano Squartini. Non-intrusive load monitoring by using active and reactive power in additive factorial hidden markov mod- els. Applied Energy, 208:1590–1607, 2015.
[20] International Energy Agency. Global ev outlook 2023. https://www.iea.org/ reports/global-ev-outlook-2023, 2023.
[21] CH Dharmakeerthi, N Mithulananthan, and TK Saha. A comprehensive planning framework for electric vehicle charging infrastructure deployment in the power grid with enhanced volt- age stability. International Transactions on Electrical Energy Systems, 25(6):1022–1040, 2015.
[22] Salman Habib, Muhammad Mansoor Khan, Farukh Abbas, Lei Sang, Muhammad Umair Shahid, and Houjun Tang. A comprehensive study of implemented international standards, technical challenges, impacts and prospects for electric vehicles. IEEe Access, 6:13866– 13890, 2018.
[23] Keshav Nath, Shashank Narayana Gowda, Chen Zhang, Rohan Shivesh Gowda, and Rajit Gadh. Short-term electric vehicle charging load forecasting using transfer and meta-learning. In 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1–5. IEEE, 2024.
[24] David Toquica, Paulo M De Oliveira-De Jesus, and Angela I Cadena. Power market equilib- rium considering an ev storage aggregator exposed to marginal prices-a bilevel optimization approach. Journal of Energy Storage, 28:101267, 2020.

[25] Hongcai Zhang, Zechun Hu, Zhiwei Xu, and Yonghua Song. Evaluation of achievable vehicle-to-grid capacity using aggregate pev model. IEEE Transactions on Power Systems, 32(1):784–794, 2016.
[26] Alberto Gasparin, Slobodan Lukovic, and Cesare Alippi. Deep learning for time series fore- casting: The electric load case. CAAI Transactions on Intelligence Technology, 7(1):1–25, 2022.
[27] Behnam Farsi, Manar Amayri, Nizar Bouguila, and Ursula Eicker. On short-term load fore- casting using machine learning techniques and a novel parallel deep lstm-cnn approach. IEEE access, 9:31191–31212, 2021.
[28] Georgios-Fotios Angelis, Christos Timplalexis, Stelios Krinidis, Dimosthenis Ioannidis, and Dimitrios Tzovaras. Nilm applications: Literature review of learning approaches, recent developments and challenges. Energy and Buildings, 261:111951, 2022.
[29] Mohammad Kaosain Akbar, Manar Amayri, Nizar Bouguila, Benoit Delinchant, and Frederic Wurtz. Evaluation of regression models and bayes-ensemble regressor technique for non- intrusive load monitoring. Sustainable Energy, Grids and Networks, 38:101294, 2024.
[30] Mohammad Kaosain Akbar, Manar Amayri, and Nizar Bouguila. Deep learning based so- lution for appliance operational state detection and power estimation in non-intrusive load monitoring. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 59–65. Springer, 2023.
[31] Shinichi Nakagawa and Robert P Freckleton. Missing inaction: the dangers of ignoring missing data. Trends in ecology & evolution, 23(11):592–596, 2008.
[32] Hyungeun Choi, Seunghyoung Ryu, and Hongseok Kim. Short-term load forecasting based on resnet and lstm. In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pages 1–6. IEEE, 2018.
[33] Tai-Yu Ma and Se´bastien Faye. Multistep electric vehicle charging station occupancy predic- tion using hybrid lstm neural networks. Energy, 244:123217, 2022.

[34] Sami Ben Brahim, Manar Amayri, and Nizar Bouguila. One-day-ahead electricity load fore- casting of non-residential buildings using a modified transformer-bilstm adversarial domain adaptation forecaster. International Journal of Dynamics and Control, 13(5):1–19, 2025.
[35] Kunjin Chen, Kunlong Chen, Qin Wang, Ziyu He, Jun Hu, and Jinliang He. Short-term load forecasting with deep residual networks. IEEE Transactions on Smart Grid, 10(4):3943– 3952, 2018.
[36] Omar Bouhamed, Maher Dissem, Manar Amayri, and Nizar Bouguila. Transformer-based deep probabilistic network for load forecasting. Engineering Applications of Artificial Intel- ligence, 152:110781, 2025.
[37] Maher Dissem and Manar Amayri. Towards efficient federated load forecasting: Personal- ization mechanisms and their impact. IEEE Internet of Things Journal, 2025.
[38] Juncheng Zhu, Zhile Yang, Yan Chang, Yuanjun Guo, Kevin Zhu, and Jianhua Zhang. A novel lstm based deep learning approach for multi-time scale electric vehicles charging load prediction. In 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), pages 3531– 3536. IEEE, 2019.
[39] Qiang Gao, Tao Zhu, Weijiang Zhou, Guanhua Wang, Tianhan Zhang, Zhi Zhang, Muham- mad Waseem, Shengyuan Liu, Chang Han, and Zhenzhi Lin. Charging load forecasting of electric vehicle based on monte carlo and deep learning. In 2019 IEEE Sustainable Power and Energy Conference (iSPEC), pages 1309–1314. IEEE, 2019.
[40] Zhiyan Yi, Xiaoyue Cathy Liu, Ran Wei, Xi Chen, and Jiangpeng Dai. Electric vehicle charging demand forecasting using deep learning model. Journal of Intelligent Transporta- tion Systems, 26(6):690–703, 2022.
[41] Vidura Sumanasena, Lakshitha Gunasekara, Sachin Kahawala, Nishan Mills, Daswin De Silva, Mahdi Jalili, Seppo Sierla, and Andrew Jennings. Artificial intelligence for electric vehicle infrastructure: Demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation. Energies, 16(5):2245, 2023.

[42] Xingshuai Huang, Di Wu, and Benoit Boulet. Ensemble learning for charging load forecast- ing of electric vehicle charging stations. In 2020 IEEE Electric Power and Energy Conference (EPEC), pages 1–5. IEEE, 2020.
[43] Djamel Eddine Mekkaoui, Mohamed Amine Midoun, and Yanming Shen. La-rcnn: Luong attention-recurrent-convolutional neural network for ev charging load prediction. Applied Intelligence, 54(5):4352–4369, 2024.
[44] Mekkaoui Djamel Eddine and Yanming Shen. A deep learning based approach for predicting the demand of electric vehicle charge. The Journal of Supercomputing, 78(12):14072–14095, 2022.
[45] Paul Banda, Muhammed A Bhuiyan, Kazi N Hasan, Kevin Zhang, and Andy Song. Time- series based deep hybrid transfer learning frameworks: a case study of electric vehicle energy prediction. In International Conference on Computational Science, pages 259–272. Springer, 2021.
[46] Antonio Liguori, Romana Markovic, Martina Ferrando, Je´roˆme Frisch, Francesco Causone, and Christoph van Treeck. Augmenting energy time-series for data-efficient imputation of missing values. Applied Energy, 334:120701, 2023.
[47] Ming-Chang Wang, Chih-Fong Tsai, and Wei-Chao Lin. Towards missing electric power data imputation for energy management systems. Expert Systems with Applications, 174:114743, 2021.
[48] Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. Re- current neural networks for multivariate time series with missing values. Scientific reports, 8(1):6085, 2018.
[49] Taeyoung Kim, Woong Ko, and Jinho Kim. Analysis and impact evaluation of missing data imputation in day-ahead pv generation forecasting. Applied Sciences, 9(1):204, 2019.
[50] Muhammad Saad, Mohita Chaudhary, Fakhri Karray, and Vincent Gaudet. Machine learning based approaches for imputation in time series data and their impact on forecasting. In 2020

IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2621–2627. IEEE, 2020.
[51] Yurui Li, Mingjing Du, and Sheng He. Attention-based sequence-to-sequence model for time series imputation. Entropy, 24(12):1798, 2022.
[52] Syed Nazir Hussain, Azlan Abd Aziz, Md. Jakir Hossen, Nor Azlina Ab Aziz, G. Ramana Murthy, and Fajaruddin Bin Mustakim. A novel framework based on cnn-lstm neural network for prediction of missing values in electricity consumption time-series datasets, Feb 2022.
[53] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27, 2014.
[54] Hyun Ahn, Kyunghee Sun, K Pio Kim, et al. Comparison of missing data imputation methods in time series forecasting. Computers, Materials & Continua, 70(1):767–779, 2022.
[55] Tlamelo Emmanuel, Thabiso Maupong, Dimane Mpoeleng, Thabo Semong, Banyatsang Mphago, and Oteng Tabona. A survey on missing data in machine learning. Journal of Big data, 8:1–37, 2021.
[56] Byungsung Lee, Haesung Lee, and Hyun Ahn. Improving load forecasting of electric vehicle charging stations through missing data imputation. Energies, 13(18):4893, 2020.
[57] Mostafa Majidpour, Peter Chu, Rajit Gadh, and Hemanshu R Pota. Incomplete data in smart grid: Treatment of missing values in electric vehicle charging data. In 2014 international conference on connected vehicles and expo (ICCVE), pages 1041–1042. IEEE, 2014.
[58] Xiaodong Shen, Houxiang Zhao, Yue Xiang, Peng Lan, and Junyong Liu. Short-term electric vehicles charging load forecasting based on deep learning in low-quality data environments. Electric Power Systems Research, 212:108247, 2022.
[59] Xiaohai Ge, Xin Zhang, and Dehong Xu. A novel seqgan-lstm load forecasting framework for electric vehicle charging stations with missing data. In 2024 IEEE 15th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pages 1–6. IEEE, 2024.

[60] Juncheng Zhu, Zhile Yang, Yuanjun Guo, Jiankang Zhang, and Huikun Yang. Short-term load forecasting for electric vehicle charging stations based on deep learning approaches. Applied sciences, 9(9):1723, 2019.
[61] Yvenn Amara-Ouali, Yannig Goude, Nathan Doume`che, Pascal Veyret, Alexis Thomas, Daniel Hebenstreit, Thomas Wedenig, Arthur Satouf, Aymeric Jan, Yannick Deleuze, et al. Forecasting electric vehicle charging station occupancy: Smarter mobility data challenge. arXiv preprint arXiv:2306.06142, 2023.
[62] H.-H. Chang, L.-S. Lin, N. Chen, and W.-J. Lee. Particle-swarm-optimization-based non- intrusive demand monitoring and load identification in smart meters. IEEE Transactions on Industry Applications, 49(5):2229–2236, 2013.
[63] S. Lin, L. Zhao, F. Li, Q. Liu, D. Li, and Y. Fu. A nonintrusive load identification method for residential applications based on quadratic programming. Electric Power Systems Research, 133:241–248, 2016.
[64] N. Batra, A. Singh, and K. Whitehouse. Neighbourhood nilm: A big-data approach to house- hold energy disaggregation. arXiv preprint arXiv:1511.02900, 2015.
[65] D. Piga, A. Cominola, M. Giuliani, A. Castelletti, and A.E. Rizzoli. Sparse optimization for automated energy end use disaggregation. IEEE Transactions on Control Systems Technol- ogy, 24(3):1044–1051, 2015.
[66] Z. Guo, Z. J. Wang, and A. Kashani. Home appliance load modeling from aggregated smart meter data. IEEE Transactions on Power Systems, 30(1):254–262, 2014.
[67] W. Kong, Z. Y. Dong, J. Ma, D. J. Hill, J. Zhao, and F. Luo. An extensible approach for nonintrusive load disaggregation with smart meter data. IEEE Transactions on Smart Grid, 9(4):3362–3372, 2016.
[68] W. Kong, Z. Y. Dong, D. J. Hill, J. Ma, J. Zhao, and F. Luo. A hierarchical hidden markov model framework for home appliance modeling. IEEE Transactions on Smart Grid, 9(4):3079–3090, 2016.

[69] H. Salem, M. Sayed-Mouchaweh, and M. Tagina. Unsupervised bayesian non parametric ap- proach for non-intrusive load monitoring based on time of usage. Neurocomputing, 435:239– 252, 2021.
[70] Oumayma Dalhoumi, Manar Amayri, and Nizar Bouguila. Energy disaggregation via bayesian non-negative matrix factorization with sum-to-k constraint. Annals of Mathematics and Artificial Intelligence, pages 1–16, 2025.
[71] Y. Zhang, H. Wu, Q. Ma, Q. Yang, and Y. Wang. A learnable image-based load signature construction approach in nilm for appliances identification. IEEE Transactions on Smart Grid, 2023.
[72] Y.-H. Lin and M.-S. Tsai. Non-intrusive load monitoring by novel neuro-fuzzy classification considering uncertainties. IEEE Transactions on Smart Grid, 5(5):2376–2384, 2014.
[73] V. Singhal, J. Maggu, and A. Majumdar. Simultaneous detection of multiple appliances from smart-meter measurements via multi-label consistent deep dictionary learning and deep transform learning. IEEE Transactions on Smart Grid, 10(3):2969–2978, 2018.
[74] L. Mauch and B. Yang. A new approach to non-intrusive load monitoring based on deep learning. In Proceedings of the IEEE, pages 63–67, 2015.
[75] M. Xia, K. Wang, X. Zhang, and Y. Xu. Non-intrusive load disaggregation based on deep dilated residual network. Electric Power Systems Research, 170:277–285, 2019.
[76] W. Kong, Z. Y. Dong, B. Wang, J. Zhao, and J. Huang. A practical solution for non-intrusive type ii load monitoring based on deep learning and post-processing. IEEE Transactions on Smart Grid, 11(1):148–160, 2019.
[77] M. Li, Z. Tu, J. Wang, P. Xu, and X. Wang. Dynamic time warping optimization-based nonintrusive load monitoring for multiple household appliances. International Journal of Electrical Power & Energy Systems, 159:110002, 2024.
[78] A. Gao et al. Non-intrusive multi-label load monitoring via transfer and contrastive learning architecture. International Journal of Electrical Power & Energy Systems, 154:109443, 2023.

[79] B. Peng et al. Integrating non-intrusive load monitoring based on graph-to-point learning into a self-adaptive home energy management system. International Journal of Electrical Power & Energy Systems, 154:109442, 2023.
[80] C. Athanasiadis, D. Doukas, T. Papadopoulos, and A. Chrysopoulos. A scalable real-time non-intrusive load monitoring system for the estimation of household appliance power con- sumption. Energies, 14(3):767, 2021.
[81] C. L. Athanasiadis, T. A. Papadopoulos, G. C. Kryonidis, and D. I. Doukas. A holistic and personalized home energy management system with non-intrusive load monitoring. IEEE Transactions on Consumer Electronics, 2024.
[82] Skander Chouchene, Manar Amayri, and Nizar Bouguila. Federated learning based sparse coding for non-intrusive load monitoring. In 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS), pages 1–6. IEEE, 2024.
[83] N. Virtsionis Gkalinikis, C. Nalmpantis, and D. Vrakas. Torch-nilm: An effective deep learning toolkit for non-intrusive load monitoring in pytorch. Energies, 15(7):2647, 2022.
[84] R. Gopinath and M. Kumar. Deepedge-nilm: A case study of non-intrusive load monitoring edge device in commercial building. Energy and Buildings, 294:113226, 2023.
[85] Soudabeh Tabarsaii, Manar Amayri, Nizar Bouguila, and Ursula Eicker. Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation. Jour- nal of Ambient Intelligence and Humanized Computing, 15(9):3359–3378, 2024.
[86] C. Li, K. Zheng, H. Guo, and Q. Chen. A mixed-integer programming approach for industrial non-intrusive load monitoring. Applied Energy, 330:120295, 2023.
[87] D. Li et al. Transfer learning for multi-objective non-intrusive load monitoring in smart building. Applied Energy, 329:120223, 2023.
[88] T. Todic, V. Stankovic, and L. Stankovic. An active learning framework for the low-frequency non-intrusive load monitoring problem. Applied Energy, 341:121078, 2023.

[89] R. Jiao, C. Li, G. Xun, T. Zhang, B. B. Gupta, and G. Yan. A context-aware multi-event identification method for non-intrusive load monitoring. IEEE Transactions on Consumer Electronics, 2023.
[90] G. Liu et al. Closed-loop learning for accuracy improvement of non-intrusive load monitoring in smart homes. IEEE Transactions on Instrumentation and Measurement, 2023.
[91] Y. Liu, L. Zhong, J. Qiu, J. Lu, and W. Wang. Unsupervised domain adaptation for nonin- trusive load monitoring via adversarial and joint adaptation network. IEEE Transactions on Industrial Informatics, 18(1):266–277, 2021.
[92] L. Yang, Y. Meng, Q. Zhang, and J. Jian. Distributionally robust deep network based on ϕ- divergence for non-intrusive load monitoring. Electric Power Systems Research, 228:110072, 2024.
[93] B. Zhao, M. Ye, L. Stankovic, and V. Stankovic. Non-intrusive load disaggregation solutions for very low-rate smart meter data. Applied Energy, 268:114949, 2020.
[94] Y. Zhang, W. Qian, Y. Ye, Y. Li, Y. Tang, Y. Long, and M. Duan. A novel non-intrusive load monitoring method based on resnet-seq2seq networks for energy disaggregation of dis- tributed energy resources integrated with residential houses. Applied Energy, 349:121703, 2023.
[95] A. Langevin, M. A. Carbonneau, M. Cheriet, and G. Gagnon. Energy disaggregation using variational autoencoders. Energy and Buildings, 254:111623, 2022.
[96] A. Faustine, L. Pereira, H. Bousbiat, and S. Kulkarni. Unet-nilm: A deep neural network for multi-tasks appliances state detection and power estimation in nilm. In Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, pages 84–88, November 2020.
[97] Heng Guo, Qiushi Cui, Zihong Xie, Yao Liu, Lixian Shi, and Qianggang Wang. Deep trans- lation model-based electric vehicle load disaggregation method. In 2023 IEEE Sustainable Power and Energy Conference (iSPEC), pages 1–6. IEEE, 2023.

[98] Shengyi Wang, Liang Du, Jin Ye, and Dongbo Zhao. Robust identification of ev charging profiles. In 2018 IEEE Transportation Electrification Conference and Expo (ITEC), pages 1–6. IEEE, 2018.
[99] Attique Ur Rehman, Tek Tjing Lie, Brice Valle`s, and Shafiqur R Tito. Low complexity non- intrusive load disaggregation of air conditioning unit and electric vehicle charging. In 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), pages 2607–2612. IEEE, 2019.
[100] Yi Wang, Chunwei Ma, Bochao Zhao, and Wenpeng Luan. Non-intrusive electric vehicle charging load disaggregation based on independent component analysis with reference. In 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), pages 490–495. IEEE, 2021.
[101] Camilo Marin˜o, Guillermo Cossio, Pablo Massaferro, Matias Di Martino, Alvaro Go´mez, and Alicia Fernandez. Nilmev: Electric vehicle disaggregation for residential customer en- ergy efficiency incentives. In 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1–5. IEEE, 2023.
[102] Mark Jenkins and Ivana Kockar. Electric vehicle aggregation model: A probabilistic ap- proach in representing flexibility. Electric Power Systems Research, 213:108484, 2022.
[103] Michael Pertl, Francesco Carducci, Michaelangelo Tabone, Mattia Marinelli, Sila Kiliccote, and Emre C Kara. An equivalent time-variant storage model to harness ev flexibility: Forecast and aggregation. IEEE transactions on industrial informatics, 15(4):1899–1910, 2018.
[104] M Hadi Amini, Amin Kargarian, and Orkun Karabasoglu. Arima-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electric Power Systems Research, 140:378–390, 2016.
[105] Nikita Korolko, Zafer Sahinoglu, and Daniel Nikovski. Modeling and forecasting self-similar power load due to ev fast chargers. IEEE Transactions on Smart Grid, 7(3):1620–1629, 2015.
[106] Henry M Louie. Time-series modeling of aggregated electric vehicle charging station load.
Electric Power Components and Systems, 45(14):1498–1511, 2017.

[107] Mostafa Majidpour, Charlie Qiu, Peter Chu, Hemanshu R Pota, and Rajit Gadh. Forecasting the ev charging load based on customer profile or station measurement? Applied energy, 163:134–141, 2016.
[108] Yiqi Lu, Yongpan Li, Da Xie, Enwei Wei, Xianlu Bao, Huafeng Chen, and Xiancheng Zhong. The application of improved random forest algorithm on the prediction of electric vehicle charging load. Energies, 11(11):3207, 2018.
[109] Hamidreza Jahangir, Hanif Tayarani, Ali Ahmadian, Masoud Aliakbar Golkar, Jaume Miret, Mohammad Tayarani, and H Oliver Gao. Charging demand of plug-in electric vehicles: Fore- casting travel behavior based on a novel rough artificial neural network approach. Journal of cleaner production, 229:1029–1044, 2019.
[110] Morteza Dabbaghjamanesh, Amirhossein Moeini, and Abdollah Kavousi-Fard. Reinforce- ment learning-based load forecasting of electric vehicle charging station using q-learning technique. IEEE Transactions on Industrial Informatics, 17(6):4229–4237, 2020.
[111] Juncheng Zhu, Zhile Yang, Monjur Mourshed, Yuanjun Guo, Yimin Zhou, Yan Chang, Yanjie Wei, and Shengzhong Feng. Electric vehicle charging load forecasting: A comparative study of deep learning approaches. Energies, 12(14):2692, 2019.
[112] Hamidreza Jahangir, Saleh Sadeghi Gougheri, Behzad Vatandoust, Masoud Aliakbar Golkar, Ali Ahmadian, and Amin Hajizadeh. Plug-in electric vehicle behavior modeling in energy market: A novel deep learning-based approach with clustering technique. IEEE Transactions on Smart Grid, 11(6):4738–4748, 2020.
[113] Qiang Gao, Tao Zhu, Weijiang Zhou, Guanhua Wang, Tianhan Zhang, Zhi Zhang, Muham- mad Waseem, Shengyuan Liu, Chang Han, and Zhenzhi Lin. Charging load forecasting of electric vehicle based on monte carlo and deep learning. In 2019 IEEE Sustainable Power and Energy Conference (iSPEC), pages 1309–1314. IEEE, 2019.
[114] Belal Mahmud Fahim, Mohammad Kaosain Akbar, and Manar Amayri. Residualnet: A novel electric vehicle charging data imputation technique to enhance load forecasting accuracy. Building Simulation, 18(4):897–922, 2025.

[115] Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacˇic´, Thomas Y Hou, and Max Tegmark. Kan: Kolmogorov-arnold networks. arXiv preprint arXiv:2404.19756, 2024.
[116] Sidharth SS, Keerthana AR, Gokul R, and Anas KP. Chebyshev polynomial-based kolmogorov-arnold networks: An efficient architecture for nonlinear function approxima- tion, 2024.
[117] Alireza Afzal Aghaei. fkan: Fractional kolmogorov–arnold networks with trainable jacobi basis functions. Neurocomputing, 623:129414, 2025.
[118] Alexander Dylan Bodner, Antonio Santiago Tepsich, Jack Natan Spolski, and Santiago Pourteau. Convolutional kolmogorov-arnold networks, 2025.
[119] Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, and Ma`rius Caus. Kolmogorov-arnold networks (kans) for time series analysis, 2024.
[120] Belal Mahmud Fahim, Mohammad Kaosain Akbar, and Manar Amayri. Residualnet: A novel electric vehicle charging data imputation technique to enhance load forecasting accuracy. In Building Simulation, pages 1–26. Springer, 2025.
[121] Fuad Un-Noor, Sanjeevikumar Padmanaban, Lucian Mihet-Popa, Mohammad Nurunnabi Mollah, and Eklas Hossain. A comprehensive study of key electric vehicle (ev) compo- nents, technologies, challenges, impacts, and future direction of development. Energies, 10(8):1217, 2017.
[122] K Liu, S Subbarayan, RR Shoults, MT Manry, C Kwan, FI Lewis, and J Naccarino. Compar- ison of very short-term load forecasting techniques. IEEE Transactions on power systems, 11(2):877–882, 1996.
[123] Xiaolong Xu, Weizhi Chong, Shancang Li, Abdullahi Arabo, and Jianyu Xiao. Miaec: Miss- ing data imputation based on the evidence chain. IEEE Access, 6:12983–12992, 2018.
[124] Dusˇan Sovilj, Emil Eirola, Yoan Miche, Kaj-Mikael Bjo¨rk, Rui Nian, Anton Akusok, and

Amaury Lendasse. Extreme learning machine for missing data using multiple imputations.
Neurocomputing, 174:220–231, 2016.

[125] Edgar Acuna and Caroline Rodriguez. The treatment of missing values and its effect on classifier accuracy. In Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), Illinois Institute of Technology, Chicago, 15–18 July 2004, pages 639–647. Springer, 2004.
[126] Gustavo EAPA Batista, Maria Carolina Monard, et al. A study of k-nearest neighbour as an imputation method. His, 87(251-260):48, 2002.
[127] Lorenzo Beretta and Alessandro Santaniello. Nearest neighbor imputation algorithms: a critical evaluation. BMC medical informatics and decision making, 16:197–208, 2016.
[128] Yangyang Wu, Jun Wang, Xiaoye Miao, Wenjia Wang, and Jianwei Yin. Differentiable and scalable generative adversarial models for data imputation. IEEE Transactions on Knowledge and Data Engineering, 2023.
[129] Cheng Fan, Fu Xiao, Yang Zhao, and Jiayuan Wang. Analytical investigation of autoencoder- based methods for unsupervised anomaly detection in building energy data. Applied energy, 211:1123–1135, 2018.
[130] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11461– 11471, 2022.
[131] Alpaslan Demirci, Said Mirza Tercan, Umit Cali, and Ismail Nakir. A comprehensive data analysis of electric vehicle user behaviors toward unlocking vehicle-to-grid potential. IEEE Access, 11:9149–9165, 2023.
[132] Sridevi Tirunagari, Mingchen Gu, and Lasantha Meegahapola. Reaping the benefits of smart electric vehicle charging and vehicle-to-grid technologies: Regulatory, policy and technical aspects. IEEE Access, 10:114657–114672, 2022.

[133] Zhihong Shen, Ling Ling, Liang Zhang, Shilong Shen, Yihan Huang, Sanhe Fu, Ruisheng Diao, and Haobo Li. Intelligent clustering and trending analysis of ev charging behavior in active distribution power network. In 2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2), pages 623–628. IEEE, 2023.
[134] Sina Ibne Ahmed, Hossein Salehfar, and Daisy Flora Selvaraj. Impact of electric vehicle charging on the performance of distribution grid. In 2021 IEEE 12th International Sympo- sium on Power Electronics for Distributed Generation Systems (PEDG), pages 1–8. IEEE, 2021.
[135] Julia´n Luengo, Salvador Garc´ıa, and Francisco Herrera. A study on the use of imputation methods for experimentation with radial basis function network classifiers handling miss- ing attribute values: The good synergy between rbfns and eventcovering method. Neural Networks, 23(3):406–418, 2010.
[136] Lokesh Borawar and Ravinder Kaur. Resnet: Solving vanishing gradient in deep networks. In Proceedings of International Conference on Recent Trends in Computing: ICRTC 2022, pages 235–247. Springer, 2023.
[137] Muskaan Pirani, Paurav Thakkar, Pranay Jivrani, Mohammed Husain Bohara, and Dweepna Garg. A comparative analysis of arima, gru, lstm and bilstm on financial time series fore- casting. In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pages 1–6. IEEE, 2022.
[138] Zachary J Lee, Tongxin Li, and Steven H Low. Acn-data: Analysis and applications of an open ev charging dataset. In Proceedings of the tenth ACM international conference on future energy systems, pages 139–149, 2019.
[139] Zachary J. Lee, Tongxin Li, and Steven H. Low. ACN-Data: Analysis and Applications of an Open EV Charging Dataset. In Proceedings of the Tenth International Conference on Future Energy Systems, e-Energy ’19, June 2019.
[140] City of Palo Alto. Electric vehicle charging station usage (july 2011 - dec 2020). https://data.cityofpaloalto.org/dataviews/257812/

electric-vehicle-charging-station-usage-july-2011-dec-2020/, 2024. Retrieved June 7, 2024.
[141] Roderick JA Little and Donald B Rubin. Statistical analysis with missing data, volume 793. John Wiley & Sons, 2019.
[142] Todd E Bodner. What improves with increased missing data imputations? Structural equa- tion modeling: a multidisciplinary journal, 15(4):651–675, 2008.
[143] Alireza Amirteimoori and Sohrab Kordrostami. A euclidean distance-based measure of effi- ciency in data envelopment analysis. Optimization, 59(7):985–996, 2010.
[144] Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tib- shirani, David Botstein, and Russ B Altman. Missing value estimation methods for dna microarrays. Bioinformatics, 17(6):520–525, 2001.
[145] Richard O Duda, Peter E Hart, et al. Pattern classification. John Wiley & Sons, 2006.

[146] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014.
[147] Ahmed Zakariae Hinchi and Mohamed Tkiouat. Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network. Procedia Com- puter Science, 127:123–132, 2018.
[148] Abien Fred Agarap. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375, 2018.
[149] Mike Schuster and Kuldip K Paliwal. Bidirectional recurrent neural networks. IEEE trans- actions on Signal Processing, 45(11):2673–2681, 1997.
[150] Neelam Mughees, Syed Ali Mohsin, Abdullah Mughees, and Anam Mughees. Deep se- quence to sequence bi-lstm neural networks for day-ahead peak load forecasting. Expert Systems with Applications, 175:114844, 2021.

[151] Ga´bor Melis, Toma´sˇ Kocˇisky`, and Phil Blunsom. Mogrifier lstm. arXiv preprint
arXiv:1909.01792, 2019.

[152] Minhao Liu, Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, and Qiang Xu. Scinet: Time series modeling and forecasting with sample convolution and interaction. Ad- vances in Neural Information Processing Systems, 35:5816–5828, 2022.
[153] Tianfeng Chai and Roland R Draxler. Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature. Geoscientific model development, 7(3):1247–1250, 2014.
[154] Cort J Willmott and Kenji Matsuura. Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate research, 30(1):79–82, 2005.
[155] Nico JD Nagelkerke et al. A note on a general definition of the coefficient of determination.
biometrika, 78(3):691–692, 1991.

[156] Oliver Parson, Grant Fisher, April Hersey, Nipun Batra, Jack Kelly, Amarjeet Singh, William Knottenbelt, and Alex Rogers. Dataport and nilmtk: A building data set designed for non- intrusive load monitoring. In 2015 ieee global conference on signal and information process- ing (globalsip), pages 210–214. IEEE, 2015.
[157] Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang. Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the european conference on computer vision (ECCV), pages 464–479, 2018.
[158] Monica M Eskander and Carlos A Silva. A complementary unsupervised load disaggregation method for residential loads at very low sampling rate data. Sustainable Energy Technologies and Assessments, 43:100921, 2021.
[159] Adisa Dedic´, Tatjana Konjic´, Martin C´ alasan, and Zehrudin Dedic´. Fuzzy c-means clustering applied to load profiling of industrial customers. Electric Power Components and Systems, 49(11-12):1068–1084, 2022.

[160] Lucas Pereira and Nuno Nunes. Performance evaluation in non-intrusive load monitoring: datasets, metrics, and tools—a review. Wiley Interdisciplinary Reviews: data mining and knowledge discovery, 8(6):e1265, 2018.
[161] Alon Harell, Stephen Makonin, and Ivan V Bajic´. Wavenilm: A causal neural network for power disaggregation from the complex power signal. In ICASSP 2019-2019 IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8335–8339. IEEE, 2019.
[162] European Environment Agency. Greenhouse gas emissions from transport in europe. https://www.eea.europa.eu/en/analysis/indicators/ greenhouse-gas-emissions-from-transport, April 2025. Chart updated 14 April 2025; published 4 November 2024.
[163] Nicolo` Daina, Aruna Sivakumar, and John W Polak. Modelling electric vehicles use: a survey on the methods. Renewable and Sustainable Energy Reviews, 68:447–460, 2017.
[164] Xiaohua Wu, Xiaosong Hu, Xiaofeng Yin, and Scott J Moura. Stochastic optimal energy management of smart home with pev energy storage. IEEE Transactions on Smart Grid, 9(3):2065–2075, 2016.
[165] Dai Wang, Xiaohong Guan, Jiang Wu, Pan Li, Peng Zan, and Hui Xu. Integrated energy exchange scheduling for multimicrogrid system with electric vehicles. IEEE Transactions on Smart Grid, 7(4):1762–1774, 2015.
[166] Shiwei Xia, SQ Bu, Xiao Luo, Ka Wing Chan, and Xi Lu. An autonomous real-time charg- ing strategy for plug-in electric vehicles to regulate frequency of distribution system with fluctuating wind generation. IEEE Transactions on Sustainable Energy, 9(2):511–524, 2017.
[167] Riccardo Remo Appino, Miguel Mun˜oz-Ortiz, Jorge A´ ngel Gonza´lez Ordiano, Ralf Mikut, Veit Hagenmeyer, and Timm Faulwasser. Reliable dispatch of renewable generation via charging of time-varying pev populations. IEEE Transactions on Power Systems, 34(2):1558–1568, 2018.

[168] Aleksandar Janjic, Lazar Velimirovic, Miomir Stankovic, and Andrija Petrusic. Commercial electric vehicle fleet scheduling for secondary frequency control. Electric Power Systems Research, 147:31–41, 2017.
[169] Katarina Knezovic´, Sergejus Martinenas, Peter Bach Andersen, Antonio Zecchino, and Mat- tia Marinelli. Enhancing the role of electric vehicles in the power grid: Field validation of multiple ancillary services. IEEE Transactions on Transportation Electrification, 3(1):201– 209, 2016.
[170] Mehdi Zeraati, Mohamad Esmail Hamedani Golshan, and Josep M Guerrero. A consensus- based cooperative control of pev battery and pv active power curtailment for voltage regula- tion in distribution networks. IEEE Transactions on Smart Grid, 10(1):670–680, 2017.
[171] Arash Asrari, Meisam Ansari, Javad Khazaei, and Poria Fajri. A market framework for decentralized congestion management in smart distribution grids considering collaboration among electric vehicle aggregators. IEEE Transactions on Smart Grid, 11(2):1147–1158, 2019.
[172] Huijun Liang, Yungang Liu, Fengzhong Li, and Yanjun Shen. Dynamic economic/emission dispatch including pevs for peak shaving and valley filling. IEEE Transactions on Industrial Electronics, 66(4):2880–2890, 2018.