# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 1 Introduction
The diversity of probability distribution at different time points can provide power system policymakers with an abundance of uncertain information, including economic dispatch, rotating standby arrangement, and electricity market price optimization problems. In a word, the probabilistic prediction method can give the possible PV power value and its probability distribution in the next moment, and provide more-comprehensive prediction information [26]. However, the current research on probability prediction in the field of photovoltaic power generation is still in its infancy. [PERSON] et al. [27] proposed two parametric probability prediction methods for predicting solar irradiance by \(\beta\)-distribution and bilateral power distribution, effectively predicting solar irradiance and accurately describing its stochastic characteristics. [PERSON] et al. [28] assumed the prediction error distribution as the normal distribution and the Laplacian distribution. The probability distribution of the generated power and the confidence interval value at different confidence levels was then obtained by the maximum likelihood estimation method. [PERSON] et al. [29] used the meteorological data obtained by NWP as input data, and a probability prediction model based on a quantile regression prediction algorithm was established to study the probability prediction of photovoltaic power generation. [PERSON] et al. [30] combined the probability distribution theory with the Gaussian mixture method, and the prediction results are consistent with the actual probability distribution of photovoltaic power under different weather conditions. Considering that PV output is affected by many weather factors, and that the complexity of a model will increase if too many variables exist, this paper preprocesses the original data set based on feature selection and similar sample classifications. It is difficult for the point prediction method to express the uncertainty of prediction results. Two improved photovoltaic power generation probabilistic prediction models are proposed in this paper to improve the preliminary prediction performance. The idea of traditional combination methods is to combine different algorithms to optimize a certain prediction model without changing the essential structure of the models. Through analysis, it was found that different models had different advantages during different periods of time. In order to improve the prediction accuracy by contributing to the advantages of the different models, a new probability prediction model with multi-interval and variable weight is proposed. The method is applied to handle the uncertainties of photovoltaic power generation for the first time, to the best of our knowledge. The major contributions and innovations of this paper are as follows:  
1. The improved grey wolf optimization algorithm is used to optimize the sparse Gaussian process model and the least squares support vector machine model. The better super-parameter values are obtained, which improve accuracy. 2. An error correction probability prediction model based on improved least squares support vector machine (IMLSSVM) is proposed. The model can be corrected by using the historical error distribution to realize the probability prediction model under the premise of realizing point prediction. 3. A piecewise optimal combination model is proposed that uses different combined weighting coefficients in different periods to make full use of different prediction models.