# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 1 Introduction  
At present, the average natural gas storage in the world is 53 years. There is more coal in storage than oil and natural gas, and the world's coal storage capacity is 15,980 tons, which can be mined for about 200 years [1]. It can be seen that a shortage of fossil fuels is imminent. Photovoltaic (PV) power generation has developed rapidly in the world in recent years due to its advantages in meeting energy demands, reducing environmental pollution, and improving energy structure [2]. As a result of solar radiation and other factors, PV power output has high volatility and randomness, and with the increase of photovoltaic grid-connected capacity, this adverse impact will bring more and more risks to grid operation. Therefore, accurate prediction of photovoltaic power generation will be of great significance to the stability and safety of grid dispatching and power systems [3]. In many previous studies, the prediction of photovoltaic power generation based on physical models has shown great progress. [PERSON] et al. [4] established the basic model of the photovoltaic cell and photoelectric conversion efficiency based on the principle of photovoltaic power generation and photoelectric conversion efficiency model. Then empirical formulas affecting photoelectric conversion efficiency were obtained, and reasonable empirical parameters were selected to predict PV output power. [PERSON] et al. [5] proposed the three-parameter, four-parameter, and five-parameter equivalent circuit model comparison method for photovoltaic cells and two thermal models for estimating battery temperature, which can achieve better accuracy with fewer parameter solutions. However, the creation of physical model and parameter solving process are complicated, and the anti-interference ability of the model is poor. With the development of machine learning technology and computer hardware capabilities, an increasing number of machine learning and statistical regression methods have been applied to the field of photovoltaic power generation prediction. This includes artificial neural network algorithms [6], support vector machine (SVM) algorithms [7; 8], the ARMAX algorithm [9], the Markov chain algorithm [10], and so on. [PERSON] et al. [11] proposed a neural network-based photovoltaic power generation prediction model that can predict power generation under different weather conditions one day in advance. [PERSON] et al. [12] proposed a solar irradiance prediction method based on the support vector machine algorithm. This method uses different kernel functions to predict solar irradiance, which are then compared. Based on the ARMAX algorithm, [PERSON] et al. [13] considered meteorological factors to obtain more-accurate prediction results. A photovoltaic power generation prediction model based on an improved Markov chain was proposed by [PERSON] [14]. The Markov chain is mainly used to correct the residual of the prediction model to improve accuracy. However, these algorithms have obvious defects. It is easy for the neural network algorithm to fall into the local minimum, and the model is not well explained. The support vector machine has limited processing ability for large samples of data, and the time series method has weak non-stationary processing ability. Many scholars have improved the efficiency of models by improving the algorithm. [PERSON] et al. [15] used the particle swarm optimization (PSO) to optimize the normalization parameters and kernel parameters of SVM. The back propagation (BP) neural network algorithm was improved by combining the momentum term with the variable learning rate, so the defects--the traditional BP learning algorithm is liable to fall into local minimum points, and has a slow convergence rate--were remedied. The improved BP neural network is used to improve the predictive performance [16]. The output of PV generation is restricted by its external environment, and the weather has a greater impact on photovoltaic systems.