# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 1 Introduction
In order to reduce the impact of weather, data are classified according to the type of the weather. Some results prove that such methods have a better performance [17; 18]. In most models, only conventional variables such as temperature, humidity, and wind speed are generally considered, so the accuracy of these models will decrease under extreme weather conditions. The aerosol index (AI) can indicate that there is a strong linear relationship between particulate matter in the atmosphere and solar radiation attenuation, which has a potential impact on the energy generated by photovoltaic panels. In [19], based on the classification of seasons, AI can be used as an additional input parameter to adapt to the complicated environment. The drawback of these methods, then, is that the accuracy of models depends largely on the accuracy of the weather classification. The accuracy of predictive models based on statistical learning depends mainly on a large amount of historical data. However, historical data contains complex information, and there is redundant information that may not be necessary. Not all weather factors have a significant impact on PV output, so we need to extract or remove information from historical data to reduce the complexity of models. In [20], it is shown that temperature and insolation are positively correlated with PV power, humidity is negatively correlated with PV power, and wind speed has no obvious correlation with PV power by the correlation analysis. Therefore, air temperature, humidity, insolation, and historical PV power can be selected as inputs to the prediction model to reduce the complexity of the data. [PERSON] et al. [21] proposed a PV output prediction method combining wavelet decomposition and the artificial neural network (ANN) algorithm. After separating the useful information and the interference information by wavelet decomposition, the neural network model is used to obtain the predicted power value. [PERSON] et al. [22] combined the quadratic Renyi entropy criteria with principal component analysis (PCA) to reasonably reduce the data dimension and use least squares support vector machines (LSSVM) to predict future PV power. This model can facilitate calculation, while improving accuracy. In addition, it has been found that the introduction of image data can also improve prediction accuracy. [PERSON] et al. [23] proposed a method for combining solar cloud image data and artificial neural network models to predict solar irradiance. [PERSON] et al. [24] used cloud information and cloud maps in numerical weather prediction (NWP) to predict the power attenuation caused by cloud clusters blocking photovoltaic power plants over the subsequent 4 hours,then corrected the predicted values to improve the accuracy of model prediction. This type of method requires more advanced experimental equipment, however. Most of the research has aimed to predict a certain value at a certain moment, but it is difficult for point prediction values to express the uncertainty of the prediction result, which will affect power grid scheduling and the stability of the power system. Compared with point prediction, probabilistic prediction makes up for the shortcoming that point prediction cannot measure the uncertainty of prediction results [25]. Due to the uncertainty of solar resources and the inherent defects of the prediction model, the point prediction error of solar power cannot be avoided, and the defect that the point prediction result cannot make a quantitative description of the uncertainty of solar power is difficult to overcome. In terms of the application of solar power, there needs to be a relatively accurate estimation of the fluctuation range of solar power, which requires planning, operation, safety, and stability analysis of the power grid (including solar power generation). The probabilistic prediction of solar power generation expands the connotations of solar power generation prediction, and can provide the probability distribution of PV power generation.