# The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
## 1 Introduction
### Literature Review  
In recent years, there has been an increasing amount of research conducted by scholars on power forecasting models. Currently, these models can be broadly classified into three main categories. The first category comprises functional statistical models (FSM), followed by regression prediction models (RPM) as the second category, and deep learning (DL) prediction models based on big data as the third category. The functional statistical model of power forecasting comprises various techniques such as the Kalman filter [8], regression analysis [9; 10; 11], and time series methods [12]. [PERSON] et al. (2016) [8] employed the ensemble Kalman filter (EnKF) technique and shrinkage/multiple regression methods to develop a power load modeling framework. This framework enabled the prediction and analysis of power load conditions. While the Kalman filter method for electricity forecasting can mitigate the impact of data noise on the forecasting results, it may not achieve optimal performance in nonlinear scenarios. [PERSON] et al. (1998) [9] proposed a nonparametric regression-based forecasting method that can predict future load data using historical load data. [PERSON] (2001) [10] proposed a local polynomial regression-based load forecasting method that utilized a data mining algorithm with a robust location estimator (M-estimator) to preprocess historical load data and approximate the functional relationship between load and load influencing factors through Taylor expansions. [PERSON] (2016) [11] introduced a local linear regression model that simplified the load forecasting problem by using linear regression to model the relationship between individual variables (modes) in the current data neighborhood. Regression analysis methods are simple to implement and encompass multiple factors affecting the load, but they require complete and error-free historical data. Additionally, these methods do not account for factors such as temporal effects and lack autonomous learning capabilities. [PERSON] et al. (2016) [12] proposed a multi-equation time series model based on least squares, which incorporated seasonal models with intra-day dependence to address the characteristics of the load in the posterior. This model effectively tackles the load forecasting problem with a limited amount of historical data, thereby enhancing efficiency. However, the time series model may be less sensitive to factors such as climate and environment. Unlike functional statistical models, regression predictive models are often better equipped to handle nonlinear data. As research progresses, some scholars have proposed the utilization of regression learning models for power prediction. For example, [PERSON] et al. [13] proposed a convolutional neural network (CNN) combined with the K-means algorithm [14] for electricity load forecasting. This approach solved the problem of electricity forecasting models not adapting to large datasets. However, it often led to the electricity forecasting results reaching local optimum rather than the global optimum. [PERSON] et al. [15] and [PERSON] et al. [16] used particle swarm and simulated annealing particle swarm optimization algorithms, respectively, to optimize the SVM algorithm for power load forecasting. These approaches improved the convergence ability of the model and reduced computing time. However, such algorithms are not applicable to medium and large datasets. In recent years, the use of deep learning algorithms based on big data has garnered significant attention from researchers in the field of time series forecasting. For instance, [PERSON] et al. [17] used LSTM networks for multivariate power load forecasting. They predicted power load values at future moments using power load data with exogenous variables such as historical temperature, humidity, and wind speed. [PERSON] et al.