### Literature Review  
With the fast advancement of forecasting theories [8; 9], solar physics [10], stochastic learning [11], and machine learning [12], the relevant technology of the solar irradiance forecasting research area has also developed rapidly. In general, the existing various forecasting models are correspondingly designed for solar PV prediction with different time horizon. For example, the forecasting horizon of Numerical Weather Prediction (NWP) forecasting models is from several hours to several days [13]. Time series forecasting models generate forecast outputs with a time scale that ranges from 5 min to 6 h [14]. Statistical forecasting models based on cloud motion images and satellite information can generate PV forecast value with a time sclerosis of 6 h [15]. In this paper, we focus on day-ahead solar irradiance forecasting which the forecasting horizon is 24 h.  
Among the previous studies, solar irradiance forecasting approaches can be generally divided into several categories: statistical approaches, physical approaches and machine learning approaches and ensemble approaches. In physical approaches, three kinds of basic methods are NWP forecasting model [16], Total Sky Imagery (TSI) [17] and cloud moving based satellite imagery models, which can also help to estimate the output power of distributed PV system [18]. These kinds of physical based forecasting models require additional information about the sky image. As for the statistical approaches, persistence forecasting, time series, and Model Output Statistics (MOS) models [19] are involved. In this model, it is supposed that the forecasting data at time \(t+1\) is equal to the historical data at time \(t\). Time series approaches primarily aim at the modeling of long-term solar irradiance forecast, which includes Moving Average (MA), Autoregressive (AR) [20], Autoregressive Moving Average (ARMA) [21], and Autoregressive Integrated Moving Average (ARIMA) [22] models. The time series forecasting model only requires historical irradiance data, in which the relevant meteorological factors are not involved. In addition, time series approaches can merely capture linear relationships and require stationary input data or stationary differencing data. In recent years, machine learning based forecasting methods have also been successfully applied in many fields [23; 24; 25; 26]. Machine learning models that have been done widely applied in solar forecasting field are non-linear regression models such as Artificial Neural Network (ANNs) [27; 28], the Support Vector Machine (SVM) [29], and the Markov chain [30]. These nonlinear regression models are also frequently used together with the classification models [31]. Regarding the ensemble approach, this kind of integrated model consists of multiple trained forecasting sub-models. Additionally, all the outputs of these forecasting sub-models are taken into consideration to determine the best output of the ensemble model. This method can well leverage the advantages of different forecasting sub-models to achieve the performance optimization of the ensemble model to provide better forecasting results for application [32; 33]. Based on the abovementioned forecasting theories, many researchers have carried out important research work in the field of solar irradiance forecasting and PV power forecasting (both referred to as \"solar forecasting\" in what follows). Considering this abundant literature on solar forecasting, [PERSON] et al. [34] have conducted an adequate literature review work on the history and trends in solar irradiance and PV power forecasting through text mining. Furthermore, [PERSON] et al. [35] have also reviewed the state-of-the-art of PV and solar forecasting methodologies developed over the past decade. Regarding the forecasting of grid-connected photovoltaic plant production, [PERSON] et al. [36] implemented a comparative analysis of eleven forecasting data-driven models online and offline.