## 2 Review of Recent Works for SPVG Forecasting  
Recently, various SPVG prediction models have been developed on a much-better-integrated solution for the electricity grid, as introduced in [10]. Solar PV forecasting methods can be classified into three approaches [13]: (1) physical, (2) statistical, and (3) artificial intelligence models. The physical approach is a traditional technique that has been generally measured from meteorological and geological data through numerical weather prediction [14], satellite remote sensing measurement [15], and ground measurement devices [16]. However, these techniques require a large number of data samples [17, 18, 19, 20, 21] and fitting results, which are sensitive to pathological [22, 23] factors and rely on many different types of data [18, 19, 20, 21]. The statistical and machine learning approaches are based on data-driven methods that use historical data, such as solar PV power, irradiation, humidity, and atmospheric temperature, to build the prediction model [6]. The key factor of this statistical approach is minimizing the error of future PV power output by extracting high-quality historical samples [24]. However, these data usually contain irregular errors due to the unrefined input data. Furthermore, heavy preprocessing is required, which results in complexity and cost issues [25] such as requiring an advanced sensor for accurate input data [26], and not be describing a relationship between input features and PV power output although it is relatively clear and simple [27, 28]. Machine learning is a subset of artificial intelligence (AI) that consists of techniques that enable computers to make estimations from the data and deliver AI applications [29]. Furthermore, machine learning is a popular forecasting method for time-series data, which is classified as supervised or unsupervised. The machine learning algorithm for solar PV forecasting has generally been a supervisedlearning algorithm, such as k-nearest neighbors [30], multiple linear regression [31; 32], support vector machine [33; 34; 35], decision tree [36; 37], and random forest regression [38]. However, the machine learning approach also requires a large dataset that forecasts SPVG accurately [7]. With the rapid development of the deep learning structures algorithm, the deep neural network (DNN) is an emerging area of machine learning research that has become one of the most popular fields in academia and industry [27]. Deep learning algorithms in each layer create an artificial neural network (ANN) and can learn and make intelligent decisions by themselves. The existing short-term solar PV forecasting technique overcomes traditional machine learning problems, which include slow learning speed, falling into local extrema, gradient disappearance, and lack of a time correlation [9]. However, the deep learning approach uses an ANN that is more layered than machine learning to analyze the accuracy of the data and find patterns [12]. To use these characteristics, several authors have tried to apply deep learning for the accuracy of solar PV power forecasting, such as feed-forward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), restricted Boltzmann machines, deep belief networks, and autoencoders, as shown in Table 1. To deal with the nonlinear and periodic problems of SPVG, the traditional method did not guarantee the precision of the forecasting results. According to the above literature, to improve prediction accuracy, as shown in Table 1, most researchers used one of three types of forecasting models, classified as hybrid/ensemble, optimized parameters of DNN, and DNN with preprocessing. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Forecast Horizon** & **Error Evaluation Method** & **Model/Model Type** & **Description** \\ \hline [PERSON] et al. & 2015 & 24 h ahead & MAPE, MALPE & ANN + Aerosol & [PERSON] et al.