## 1 Introduction  
To promote sustainable economic and social development, energy sources such as solar energy and wind power need to be leveraged to counteract the rapidly growing energy consumption and deteriorating environment caused by climate change. To promote increased solar energy utilization, photovoltaic (PV) power generation has been rapidly developed worldwide [(1)]. PV power generation is affected by solar radiation, temperature and other factors. It also has strong intermittency and volatility. Grid access by large-scale distributed PV power introduces significant obstacles to the planning, operation, scheduling and control of power systems. Accurate PV power prediction not only provides the basis for grid dispatch decision-making behavior, but also provides support for multiple power source space-time complementarity and coordinated control; this reduces pre-existing rotating reserve capacity and operating costs, which ensures the safety and stability of the system and promotes the optimal operation of the power grid [2]. According to the timescale, PV power forecasting can be divided into long-term, short-term, and ultra-short-term forecasts [3]. A medium-long-term forecast (i.e., with a prediction scale of several months) can provide support for power grid planning; Short-term prediction (i.e., with a prediction scale of one to four days in advance) can assist the dispatching department in formulating generator set start-stop plans. Super short-term forecast (i.e., with a prediction scale of 15 min in advance) can achieve a real-time rolling correction of the output plan curve and can provide early warning information to the dispatcher. The shorter the time scale, the more favorable the management of preventative situations and emergencies. Most of the existing literature describes short-term forecasting research with an hourly cycle. There are few reports on the ultra-short-term prediction of PV power generation [4; 5; 6]. In addition, in the previous research, PV power prediction methods mainly include the following: physical methods, statistical methods, machine learning methods, and hybrid integration methods. (1) In physical methods, numerical weather prediction (NWP) is the most widely used method, which involves more input data such as solar radiation, temperature, and other meteorological information. (2) As for the statistical approaches, their main purpose is to establish a long-term PV output prediction model. In literature [7; 8; 9; 10], the auto-regressive, auto-regressive moving average, auto-regressive integral moving average and Kalman filtering model of short-term PV prediction are respectively established based on the time series, and obtain good prediction results. The above model is mainly based on a linear model, which only requires historical PV data and does not require more meteorological factors. In addition, the time series methods can only capture linear relationships and require stationary input data or stationary differencing data. (3) Along with the rapid update of computer hardware and the development of data mining, prediction methods based on machine learning have been successfully applied in many fields. Machine learning models that have been widely applied in PV output prediction models are nonlinear regression models such as the Deep Neural Network (DNN), the Recurrent Neural Network (RNN), the Convolutional Neural Network (CNN), the Deep Belief Network (DBN) and so forth. Literature [11; 12] establishes output prediction models based on the neural network, which can consider multiple input influencing factors at the same time. The only drawback is that the network structure and parameter settings will have a great impact on the performance of the models, which limits the application of neural networks. Literature [13; 14] has analyzed various factors affecting PV power and established support vector machine (SVM) prediction models facing PV prediction.