## 1 Introduction
As a result, it is almost impossible to capture nonlinear behavior in a time series, so the prediction accuracy may be decreased as time goes by. With the booming development of artificial intelligence, the application of machine learning technology on predicting PV generation is becoming more popular. These advanced techniques include artificial neural networks (ANN), fuzzy logic (FL), support vector machines (SVM), random forest (RF) and the naive Bayesian algorithm [20, 21, 22, 23, 24, 25]. The main principle of machine learning methods is as follows. Among them, artificial neural networks are a frequently used method, mainly containing black-propagation (BP) neural networks [26], radial basis function (RBF) neural networks [27], extreme learning machine  
Figure 1: Available popular solar radiation forecasting methods. (ELM)networks [28], and long short-term memory (LSTM) neural networks [29]. Several types of elements affecting solar radiation are determined firstly as the input features, then a nonlinear and highly complex mapping relationship is constructed. Finally, the model parameters are learned according to historical data. Traditional statistical methods cannot attain the above complex representation in most situations. In contrast, machine learning methods can overcome this deficiency. Hybrid methods of solar radiation prediction mainly consist of weight-based methods and prediction-assisted methods. The former type is a combined model composed of multiple single models with the same structure. Each model gives a unique prediction, and the weighted average of the prediction results of all models is regarded as the final prediction result [30; 31]. Unlike weight-based methods, prediction assistance methods usually include two models, one for power prediction and the other for auxiliary processes, such as data filtering, data decomposition, optimal parameter selection, and residual evaluation. According to the auxiliary technology, the forecast methods can be further divided into three groups: data preprocessing techniques, parameter optimization techniques, and residual calibration techniques. Among them, data preprocessing techniques are the commonly used methods, and they mainly include principal component analysis (PCA) and cluster analysis [31; 32], the wavelet transform (WT) [33], empirical mode decomposition (EMD) [34] and variational mode decomposition (VMD) [35], etc. Reasonable selections of preprocessing methods can reduce the negative impact of the systematic error on prediction accuracy to a certain extent. In summary, each single model has its advantages and disadvantages, and the hybrid model combines advantages of different methods to obtain a better prediction performance. This paper aims to predict short-term solar radiation through a comprehensive application of machine learning techniques. Firstly, the missing values are recovered via the means of matrix completion with low-rank structure. Robust principal component analysis, a method of strong robustness to large spare noise, is employed to denoise the recovered data. Next, solar radiation data after denoising is clustered by fusing sparse subspace representation and k-nearest-neighbor. Subsequently, four artificial neural network models are used to forecast, and thus a kind of hybrid model for short-term solar radiation prediction is proposed. The main structure of the paper is organized as follows. Section 2 describes the experimental dataset and methods. Machine learning techniques for data preprocessing are introduced in Section 3. Section 4 presents several machine learning techniques for forecasting solar radiation. In Section 5, the experiments are carried out, and a comparison of experimental results is provided. Section 6 draws conclusions.