[1] ZHANG A W. The problem of clean energy consumption in the background of carbon neutralization [J]. China Power Enterprise Management, 2021(4): 19-22 (in Chinese).
[2] GE L, QIN Y, LIU J, et al. Virtual acquisition method of distributed photovoltaic data based on similarity day and BA-WNN [J]. Electric Power AutomationEquipment, 2021, 41(6): 8-14 (in Chinese).
[3] ZHAO L, LI J M, AI X M, et al. Analysis on random component extraction and statistical characteristics of photovoltaic power [J]. Automation of Electric Power Systems, 2017, 41(1): 48-56 (in Chinese).
[4] CHENG Z, LIU C, LIU L. A method of probabilistic distribution estimation of PV generation based on similar time of day [J]. Power System Technology, 2017, 41(2): 448-455 (in Chinese).
[5] PIERRO M, BUCCI F, DE FELICE M, et al. Deterministic and stochastic approaches for day-ahead solar power forecasting [J]. Journal of Solar Energy Engineering, 2017, 139(2): 021010.
[6] GULIN M, PAVLOVI′C T, VAˇSAK M. A one-dayahead photovoltaic array power production prediction with combined static and dynamic on-line correction [J]. Solar Energy, 2017, 142: 49-60.
[7] YU R Y, CHEN N, MIAO M, et al. A repair method for PV power station output data considering weather and spatial correlations [J]. Power System Technology, 2017, 41(7): 2229-2236 (in Chinese).
[8] ZHAO L, SONG Y J, ZHANG C, et al. T-GCN: A temporal graph convolutional network for traffic prediction [J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9): 3848-3858.
[9] XU B B, CHEN K T, HUANG J J, et al. A survey on graph convolutional neural network [J]. Chinese Journal of Computers, 2020, 43(5): 755-780 (in Chinese).
[10] BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral networks and locally connected networks on graphs [DB/OL]. (2014-05-21). https://arxiv.org/abs/ 1312.6203.
[11] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [DB/OL] (2017-02-22). https://arxiv.org/abs/1609.02907.
[12] ZHU K L. A novel traffic flow forecasting method based on RNN-GCN and BRB [D]. Harbin: Harbin Normal University, 2020 (in Chinese).
[13] GENG X, LI Y G, WANG L Y, et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 3656-3663.
|