## 1 Introduction  
In recent years, the scale of renewable energy power generation has expanded rapidly. Many countries are considering incorporating renewable energy into the grid [1]. Solar energy has become one of the main sources of renewable energy [2]. The narrow definition of solar energy is solar radiation [3]. Broadly speaking, solar energy also includes other forms of energy converted from the solar radiation, such as coal, oil, natural gas, hydropower, wind energy, biological energy, etc. Solar radiation is affected by the seasons and geography, and has obvious discontinuities and uncertainties [4, 5]. These characteristics are the reason that the focus of prediction must be on solar radiation prior to predicting the output of a solar system. Photovoltaic (PV) power generation is typically divided into two forms: off-grid and grid-connected. With the maturity and development of grid-connected PV technology, grid-connected PV power generation has become a mainstream trend [6]. The capacity of large-scale centralized grid-connected PV power generation systems is rapidly increasing. However, the output power of grid-connected PV power generation systems is inherently intermittent and uncontrollable. These intrinsic characteristics cause an adverse impact on the grid and seriously restrict grid-connected PV power generation [7]. At present, research on solar radiation prediction has become more and more extensive and in depth. Among various prediction methods, the simplest is the persistence method which assumes that the future solar radiation is equal to the current solar radiation. Other solar radiation prediction methods can be classified into four categories: physical methods, statistical methods, machine learning methods and hybrid methods [8, 9, 10, 11]. Figure 1 briefly summarizes four types of prediction methods on solar radiation. Among the four categories in Figure 1, the physical methods establish the solar power generation forecast model according to the geographical environment and weather data (such as temperature, humidity, pressure, etc.) [8]. These methods can be further grouped into two subcategories: numerical weather prediction (NWP) methods [12] and spatial correlation methods [13]. NWP methods use numerical simulation to predict, that is, mathematical and physical models are applied on analyzing atmospheric conditions, and high-speed computers are utilized to forecast solar radiation [14]. Under normal conditions, NWP methods probably take a long time to predict [15]. Moreover, the meteorological and environmental factors in NWP methods are the most complicated and difficult to make accurate decisions [8,16]. In current research, it has always been difficult to improve forecast accuracy. The spatial correlation methods harness the spatial correlation of solar radiation to predict solar energy of several places. It should be noted that spatial correlation methods require rich historical data to simulate complex temporal and spatial changes. In summary, NWP methods and other physical models are not suitable for use in short-term cases and in small areas, owing to long runtimes. Meanwhile, they have high demands on computing resources. The forecasting of solar radiation intensity and solar energy based on historical experimental data is more suitable for short-term prediction [17]. Statistical methods can be mainly classified into moving average (MA), autoregressive (AR) and autoregressive moving average (ARMA), autoregressive (ARMA), autoregressive conditional heteroscedasticity (ARCH) and Kalman filtering [18,19]. The above models have fast calculation speeds, a strong interpretation ability and simple structures. However, statistical methods establish rigorous mathematical relationships between the inputs and outputs, which means that they cannot learn and change prediction strategies. In addition, a large amount of historical recording is required.