## 1 Introduction
used empirical mode decomposition (EMD) to first decompose photovoltaic output and then used an artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) respectively to conduct the prediction [22]; [PERSON], [PERSON] et al. [23] and [PERSON], [PERSON]. et al. [24] both used the ensemble empirical mode decomposition (EEMD) to decompose the photovoltaic output and then used the support vector machine (SVM) to conduct the prediction on each component; [PERSON], [PERSON] et al. [25] used variational mode decomposition (VMD) to decompose the photovoltaic output into high-frequency components and low-frequency components, then used a deep belief network (DBN) and the auto-regressive and moving average (ARMA) to predict the high-frequency component and low-frequency component, respectively, and finally the DBN was used to integrate the prediction results of each frequency component. Literature [26; 27] compared the decomposition effect of EMD, EEMD and VMD, and concluded that VMD is superior to EMD and EEMD. After the photovoltaic output is decomposed by the decomposition method, since each component contains different degrees of random and intermittent owing to meteorological reasons, and the trend of photovoltaic output, some scholars began to consider the influence of some characteristic factors on the decomposition component. For example, [PERSON], [PERSON] et al. [28] established a kind of photovoltaic power generation prediction method considering seasonal fluctuation characteristic distribution by analyzing the seasonal distribution characteristics containing photovoltaic output power, absolute power deviation, and relative change rate etc., which changes over time. However, this lacks the influence of irradiance intensity, temperature and prediction foresight period on prediction results. [PERSON], [PERSON] et al. [29] considered the effects of irradiance intensity, ambient temperature, wind speed, relative humidity and other factors on the maximum photovoltaic output, but ignored the random and intermittent nature of photovoltaic output; [PERSON], [PERSON] et al. [30] used maximum relevance minimum redundancy (mRMR) to reduce the dimension of the characteristic factor, so as to quantitatively analyze the relationship between the meteorological parameter and the photovoltaic output, but only the original photovoltaic output is considered and the prediction result has no further analyzatioIn order to further improve the prediction accuracy of photovoltaic output, this paper proposes a photovoltaic output prediction model based on the \"decomposition-feature selection-prediction\". Firstly, the original photovoltaic output sequence is decomposed by VMD to obtain a plurality of intrinsic mode functions (IMFs, that is modal components) with different fluctuation characteristics. Then, the mRMR is used to determine the influence factor feature set \(J_{n}\) of each component, and then each component sequence and the corresponding impact factor feature sets are input into the DBN model for prediction, and finally the component prediction results are integrated to obtain the final photovoltaic output prediction result. The model proposed in this paper is applied to analyze and verify the photovoltaic output of a certain power generation station in Yunnan, and the results show that the method can effectively predict the photovoltaic output. The remainder of this paper is organized as follows. Section 2 describes our proposed approach and evaluating indicators. Experimental result sand comparison are presented in Section 3. The conclusion will be given in Section 4.