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References

1 
M. M. Haque, P. Wolfs, 2016, A review of high PV penetrations is lvdistribution networks: present status, impacts and mitigation measures, Renew. Sustain. Energy Rev, Vol. 62, pp. 1195-1208DOI
2 
A. Dey, B. Chakraborty, S. Dalai, K. Bhattacharya, 2022, Insights and new practices for advanced metering infrastructure and smart energy metering framework in smart grid-a case study, pp. 323-326DOI
3 
D. Chen, D. Irwin, 2018, Sundance: black-box behind-the-meter solar disaggregation, pp. 45-55DOI
4 
K. Pu, Y. Zhao, 2023, An unsupervised similarity-based method for estimating behind-the-meter solar generation, pp. 1-5DOI
5 
E. C. Kara, C. M. Roberts, M. Tabone, L. Alvarez, D. S. Callaway, E. M. Stewart, 2018, Disaggregating solar generation from feeder-level measurements, Sustainable Energy, Grids and Networks, Vol. 13, pp. 112-121DOI
6 
J. Lin, J. Ma, J. Zhu, 2022, A privacy-preserving federated learning method for probabilistic community-level behind-the-meter solar generation disaggregation, IEEE Trans. Smart Grid, Vol. 13, No. 1, pp. 268-279DOI
7 
2023, 2023 distributed system implementation plan (DSIP) updateGoogle Search
8 
N. Balakumar, L. Kristov, M. McDonnell, M. Paterson, 2024, Distribution system operator (DSO) initial studyGoogle Search
9 
F. Bu, K. Dehghanpour, Y. Yuan, Z. Wang, Y. Zhang, 2020, A data-driven game-theoretic approach for behind-the-meter PV generation disaggregation, IEEE Trans. Power Syst., Vol. 35, No. 4, pp. 3133-3144DOI
10 
F. Bu, R. Cheng, Z. Wang, 2023, A two-layer approach for estimating behind-the-meter PV generation using smart meter data, IEEE Trans. Power Syst., Vol. 38, No. 1, pp. 885-896DOI
11 
S. Hochreiter, J. Schmidhuber, 1997, Long short-term memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780DOI
12 
A. Graves, J. Schmidhuber, 2005, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks, Vol. 18, No. 5–6, pp. 602-610DOI
13 
A. Zeng, M. Chen, L. Zhang, Q. Xu, 2023, Are transformers effective for time series forecasting?, pp. 11121-11128DOI
14 
D. Dahlioui, M. B. Øgaard, A. G. Imenes, 2025, Snow impact on PV performance: assessing the zero-output challenge in cold areas, Renew. Sustain. Energy Rev., Vol. 213DOI
15 
M. Yue, T. Hong, J. Wang, 2019, Descriptive analytics-based anomaly detection for cybersecure load forecasting, IEEE Transactions on Smart Grid, Vol. 10, No. 6, pp. 5964-5974DOI
16 
K. Li, L. Wu, L. Fi, D. Wang, 2023, A TCN-based hybrid forecasting framework for hours-ahead utility-scale PV forecasting, IEEE Transactions on Sustainable Energy, Vol. 14, No. 4, pp. 2195-2207DOI
17 
C. Pavlatos, E. Makris, G. Fotis, V. Vita, V. Mladenov, 2023, Enhancing electrical load prediction using a bidirectional LSTM neural network, Electronics, Vol. 12, No. 22DOI
18 
S. Dubey, J. N. Sarvaiya, B. Seshadri, 2013, Temperature dependent photovoltaic (PV) efficiency and its effect on PV production in the world—A review, Energy Proc., Vol. 33, pp. 311-321DOI
19 
M.-H. Guo, 2023, Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, pp. 5436-5447DOI
20 
Y. Liu, 2024, iTransformer: Inverted Transformers are effective for time series forecastingDOI
21 
H. Wu, 2021, Autoformer: Decomposition transformers with auto-correlation for long-term series forecastingDOI
22 
T. Zhou, 2022, FEDformer: Frequency enhanced decomposed transformer for long-term series forecastingDOI
23 
A. Vaswani, 2017, Attention is all you needDOI
24 
V. Ekambaram, 2023, TSMixer: Lightweight MLP-Mixer model for multivariate time series forecastingDOI