## 1 Introduction  
With the fast-growing consumption of fossil fuels and the resultant environmental deterioration, we are much encouraged to use renewable energy such as solar energy, wind energy, etc. [1]. According to the statistics from the International Energy Agency [2], the global photovoltaic (PV) generation capacity growth from 2010 to 2014 has exceeded that of the last 40 years. China has been taking the lead in the global PV market since 2013, followed by Japan and the United States. Furthermore, a lower PV array price has greatly reduced the costs of building PV systems. Therefore, it is predicted that, by 2050, PV power will contribute 16% to the total energy generation capacity worldwide. Research on PV systems may provide solutions to the many challenges faced by the environment and the power system itself. Studies have been conducted in related areas of optimizing the energy structure and of improving the performance of a PV system. Despite the advantages mentioned above, a PV system is highly subject to environment pollution, and has been criticized for its unstable, random and intermittent power output [3]. When a PV system is connected to the grid, its power fluctuation may destabilize the grid and pose a threat to network security, which makes it even harder to formulate generation plans. As such, an accurate prediction of PV power output is required to make better generation plans, support the spatial and temporal compensation, and achieve coordinated power control, so that the need for energy storage capacity and operating costs can be reduced [4]. Moreover, better prediction of PV power also helps to enhance system security and stability, as well as optimize the operation of the power system [5]. The prediction methods include physical methods and statistical methods. Usually, a physical method first predicts the factors which directly influence the PV power output (such as solar radiation and ambient temperature) and then uses the forecast result as the input of the physical model to obtain the output power. On the other hand, a statistical method uses historical data to build a statistical model based on some machine learning algorithms, and predicts the PV power output directly without building a specific physical model. It has been proved that the physical method is superior to the statistical method, given large data sets [6]. Some regression models are also used to figure out the relationship between the input and output variables, such as the random forest model [7] and the support vectors regression model [8]. Researchers have built many kinds of neuron networks to predict the power production of PV systems. For example, [PERSON] has proposed an improved gray prediction model [9]. Also, the radial basis function (RBF) network has been applied to this area [10]. In another paper [11], they use a back propagation (BP) neural network to solve the prediction problem. In recent years, many hybrid methods have been proposed, which combine the advantages of both methods. In reference [12], a hybrid approach was developed to forecast short-term solar PV power based on the combination of the wavelet transform (WT) and radial basis function neural network (RBFNN). Some researchers also combined the seasonal auto-regressive integrated moving average method (SARIMA) and the support vector machines (SVM) method [13]. These models are proved to be effective in PV prediction, although many of them, such as SVM and RBFNN models, are complicated in terms of computation and very data-intensive for the network. All the models reviewed above ignored the time validation of the data, and assumed the data would not be out-of-date. In actual practice, training data are time sensitive, so the training data need to be updated in real time. Only a few published research papers have considered this fact so far. Among them, an online 24-h prediction model was proposed, which used an RBF network and classified the input variables based on the weather type [14].