## 2 Related Works  
### Related Works: Solar Power Forecasting  
This section introduces existing solar power forecasting models in their three categories: physical, statistical, and machine learning models. We also introduce how researchers extended existing models to enhance their robustness. First are the physical models, the concept of which is to first identify the factors that affect solar power generation and then use these factors for modeling. Two major branches of factors were considered: weather factors [3; 4; 5] and circuit board factors [55; 56; 57]. With regard to weather factors, [PERSON] et al. [3] developed a physical model for the central region of Chile to predict the solar refractive index and power output in an area, based on area elevation and the day's weather and cloud cover. The physical model established by [PERSON] et al. [4] indicated that the thickness and changes in cloud cover exert a direct impact on solar refraction and solar power generation. As for circuit board factors, [PERSON] et al. [55] developed a physical model based on the photovoltaics and thermal collectors of solar power generation; their model could effectively predict the current solar thermal energy collected and the power efficiency brought by conversion. For the reliability, safety, and stability of power stations, [PERSON] et al. [56] designed a physical model for three types of photovoltaic cells with different circuit board parameters to estimate electromagnetic temperature and thermal resistance. These studies all claimed that their models were effective. However, most researchers now believe that the process of searching for factors for physical models is time-consuming and prone to errors, so physical models have gradually fallen out of favor in recent years. With regard to statistical models, researchers have attempted to achieve prediction objectives using linear and nonlinear models [58; 59]. In linear models, [PERSON] and [PERSON] [60] used the least squares method and a linear regression algorithm to analyze the relationship between the average daily solar radiation and sunshine hours for each month, based on data collected from eight different cities in Malaysia. [PERSON] et al. [61] similarly used linear regression to develop an Angstrom model to predict global solar radiation. The Angstrom model was later utilized by [PERSON] et al. [62] to calculate correlation coefficients and develop a solar power model. In nonlinear models, [PERSON] et al. [6] employed statistical principles to develop a prediction model using time series data to forecast photovoltaic output power. Using the autoregressive integrated moving average (ARIMA) algorithm, [PERSON] et al. [7] developed a statistical model for energy management in solar panels. Based on the direction and scale of architecture, [PERSON] et al. [8] created a mathematical regression model to predict solar power use. Next, the popularity of machine learning models has risen because the technology has been demonstrated to be applicable to linear and nonlinear modeling for solar power forecasting [63]. [PERSON] et al. [9] employed a decision tree algorithm to predict the influence of different types of weather on solar power generation. [PERSON] et al. [10] used a decision tree algorithm to forecast the output power of a large-scale solar power plant. [PERSON] et al. [64] developed a power generator with three energy sources--that is, solar power,battery, and diesel fuel--and used a decision tree to manage the three energy sources. [PERSON] et al. [11] utilized a random forest algorithm to reasonably distribute power supplied by a solar power plant while taking safety and reliability into consideration. [PERSON] et al. [12] used the characteristics of random forest factor extraction to identify the crucial factors in solar power datasets and then performed modeling using only these crucial factors. Aside from the conventional machine learning models, many researchers have been exploring the efficacy of artificial neural networks. For instance, [PERSON] et al.