[1] Zhang W D, Liu Z M, Wang Z Z et al. Economic comparison among multiple schemes of photovoltaic flexible grid-connection based on annual irradiance data[J]. Electric Power Automation Equipment, 34, 123-129,136(2014).
[5] Jiang Y J. Modeling of photovoltaic cells and its application in energy prediction[D](2011).
[6] Zhao W J, Zhang N, Kang C Q et al. A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation[J]. Automation of Electric Power Systems, 39, 8-15(2015).
[7] Li C L, Zhu H M, Jing M D et al. Discussion on power prediction method of grid-connected PV power station[J]. Electric Engineering, 27-28(2010).
[12] Guan L, Zhao Q, Zhou B R et al. Multi-scale clustering analysis based modeling of photovoltaic power characteristics and its application in prediction[J]. Automation of Electric Power Systems, 42, 24-30(2018).
[14] Goodfellow I, Bengio Y, Courville A[M]. Deep learning(2016).
[15] Wang Y F, Fu Y C, Sun L et al. Ultra-short term prediction model of photovoltaic output power based on chaos-RBF neural network[J]. Power System Technology, 42, 1110-1116(2018).
[16] Yao H M, Du X H, Qin W P. PV power forecasting approach based on density peaks clustering and general regression neural network[J]. Acta Energiae Solaris Sinica, 41, 184-190(2020).
[17] Zhang Q, Ma Y, Li G L et al. Applications of frequency domain decomposition and deep learning algorithms in short-term load and photovoltaic power forecasting[J]. Proceedings of the CSEE, 39, 2221-2230(2019).
[18] Cao S Q, Hao W J, Wang H et al. Prediction of effective wind speed at hub of wind turbine based on lidar-Armax[J]. Laser & Optoelectronics Progress, 57, 171407(2020).
[19] Gao X D, Hu C H, Zhang J X et al. Adaptive prediction of remaining useful life for optoelectronic equipment based on nonlinear fractional Brownian motion[J]. Acta Optica Sinica, 40, 2423001(2020).
[20] Guo H M, Wu B J, Jiang X R et al. Research on detection method of modal power using photonic lantern[J]. Acta Optica Sinica, 42, 0106003(2022).
[21] Sun Z Q, Li D Y. Prediction model of photovoltaic power generation based on time-frequency entropy and neural network[J]. Journal of Central South University (Science and Technology), 51, 221-230(2020).
[22] Wang C Y, Duan Q Q, Zhou K et al. A hybrid model for photovoltaic power prediction of both convolutional and long short-term memory neural networks optimized by genetic algorithm[J]. Acta Physica Sinica, 69, 20191935(2020).
[24] Niu Z W, Yu Z Y, Li B et al. Short-term wind power forecasting model based on deep gated recurrent unit neural network[J]. Electric Power Automation Equipment, 38, 36-42(2018).
[26] Qiao Y, Sun R F, Ding R et al. Distributed photovoltaic station cluster short-term power forecasting part Ⅱ: gridding prediction[J]. Power System Technology, 45, 2210-2218(2021).
[28] Li B, Lu M Z. Short-term load forecasting modeling of regional power grid considering real-time meteorological coupling effect[J]. Automation of Electric Power Systems, 44, 60-68(2020).
[31] Li K, Li X Y, Chen S et al. Analysis oneasibility of damping HVDC-induced sub synchronous oscillation by photovoltaic grid-connection[J]. Electric Power Automation Equipment, 35, 41-46(2015).