## 1 Introduction
The results show that the SVM adopts the principle of structural risk minimization to replace the principle of empirical risk minimization of traditional neural networks; thus, it has a better generalization ability. To effectively enhance the reliability and accuracy of PV prediction results, related literature proposes the use of intelligent optimization algorithms to estimate model parameters; some examples of intelligent optimization algorithms include the gray wolf algorithm, the similar day analysis method and the particle swarm algorithm [15; 16; 17]. The example analysis illustrates the effectiveness of the optimization algorithm. (4) In recent years, decomposition-refactoring prediction models based on signal analysis methods have attracted more and more attention from scholars. Relevant research shows that using signal analysis methods to preprocess data on PV power series can reduce the influence of non-stationary meteorological external factors on the prediction results and improve prediction accuracy. The decomposition methods of PV power output data mainly include wavelet analysis and wavelet packet transform [18; 19], empirical mode decomposition (EMD) [20], ensemble empirical mode decomposition (EEMD) [21] and local mean decomposition (LMD) [22]. Among them, wavelet analysis has good time-frequency localization characteristics, but the decomposition effect depends on the choice of basic function and the self-adaptability is poor [23]. EMD has strong self-adaptability, but there are problems such as end-effects and over-enveloping [24]. LMD has fewer iterations and lighter end-effects. However, judging the condition of purely FM signals requires trial and error. If the sliding span is not properly selected, the function will not converge, resulting in excessive smoothness, which affects algorithmic accuracy [25]. EEMD is the improved EMD method; the analysis of the signal is made via a noise-assisted, weakened influence of modal aliasing. However, this method has a large amount of computation and more modal components than the true value [26]. Variational mode decomposition (VMD) is a relatively new signal decomposition method. Compared to the recursive screening mode of EMD, EEMD, and LMD, by controlling the modal center frequency K, the VMD transforms the estimation of the sequence signal modality into a non-recursive variational modal problem to be solved, which can well express and separate the weak detail signal and the approximate signal in the signal. It is essentially a set of adaptive Wiener filters with a mature theoretical basis. In addition, the non-recursive method adopted does not transmit errors, and solves the modal aliasing phenomenon of EMD, EEDM and other methods appeared in the background of bad noise, and effectively weakens the degree of end-effect [27]. Literature [28] used this method for fault diagnoses and achieved good results. Through the above literature research, we find that the previous prediction methods using traditional neural network models and single machine learning models cannot meet the performance requirements of local solar irradiance prediction scenarios with complex fluctuations. To further improve the prediction accuracy of PV output, this work proposes a new and innovative hybrid prediction method that can improve prediction performance. This method is a hybrid of variational mode decomposition (VMD), the deep belief network (DBN), and the auto-regressive moving average model (ARMA); it combines these prediction techniques adaptively. Different from the traditional PV output prediction model, the key features of the VMD-ARMA-DBN prediction model are the perfect combination of the following parts: (1) VMD-based solar radiation sequence decomposition; (2) ARMA-based low-frequency component sequence prediction model; and (3) DBN-based high-frequency component sequence prediction model. The original photovoltaic output sequences are decomposed into multiple controllable subsequences of different frequencies by using the VMD methods.