## 1 Introduction  
Solar energy development and utilization has become an important field of global energy transformation, and photovoltaic power generation has entered a stage of large-scale development, which shows a good development prospect [1]. Since the output of photovoltaic power generation is greatly affected by meteorological and other factors, it has strong intermittent and volatility. These characteristics make that high-scale photovoltaic accessing to the power system will cause huge shocks and challenges to the power system [2]. Accurate photovoltaic output prediction can provide a basis for scheduling decisions, and it is of great significance to reduce system standby and operating costs and ensure system security and economic operation. In recent years, many scholars have carried out research on short-term photovoltaic output prediction, and have achieved some results. The methods of short-term photovoltaic output prediction are mainly divided into statistical methods and heuristic learning methods [3, 4]. (1) The statistical method is to input historical data such as solar irradiance and photovoltaic power generation output. Through curve fitting and parameter estimation, the mapping model of input and output is established to realize the prediction of future photovoltaic power generation output. Common statistical methods include the time-series method [5], regression analysis method [6], grey theory [7], fuzzy theory [8] and space-time association method [9], etc. [PERSON], [PERSON] et al. studied the chaotic characteristics of photovoltaic output sequences, reconstructed the phase space of photovoltaic output information according to the short-term law of chaotic systems, and established an ultra-short-term prediction model for photovoltaic power generation [10]. [PERSON], [PERSON]. et al. considered the influence of weather type on photovoltaic output, deconstructed the law of historical photovoltaic output curve under sunny, cloudy and rainy weather patterns, and obtained the photovoltaic output benchmark curves under different weather modes, and superimposed a small-scale solar photovoltaic output benchmark curve. If the short-term fluctuations to conduct the photovoltaic power generation prediction on the benchmark curve is used, through the analysis of historical photovoltaic output data, the variation law of photovoltaic output curve in time window is extracted [11]. [PERSON], RW. et al. provides methods for identifying snowfall effects from commonly collected performance data, and recommends a model to allow for prediction of these effects based solely on meteorological time series [12]. (2) The heuristic learning method is to simulate the biological activity and use certain algorithms to train the sample data so as to obtain the relationship between input and output, that is, the prediction condition and the variable to be predicted. Common heuristic methods are the neural network [13; 14], support vector machine [15], Kalman filter algorithm [16], Markov chain [17], particle swarm algorithm [18] and genetic algorithm [19], etc. Based on numerical weather prediction (NWP) data, [PERSON], [PERSON]. et al. used a random batch gradient descent search method and a gradient accumulation strategy to accelerate the training of neural networks and achieve effective prediction of photovoltaic output [20]. A hybrid method based on an active selection of the support vectors by [PERSON], using the quadratic Renyi entropy criteria in combination with the principal component analysis (PCA), is shown to dimensionally reduce the training data in the forecasting models [21]. These methods can predict photovoltaic output well to a certain extent, but they do not fully consider the random and intermittent nature of photovoltaic output. In this regard, some scholars introduced decomposition techniques into prediction models, for example, [PERSON], [PERSON]. et al.