# The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
## 1 Introduction
### Research Gap, Contributions, and Objectives  
The Kalman filtering method for power prediction can reduce the impact of data noise on the prediction results, but the method can not achieve the optimal prediction effect in non-linear scenarios. The regression analysis method is simple, easy to operate, and covers multiple factors affecting the load, but it requires complete historical data without large errors, and the method does not take into account factors such as the decreasing influence \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Type**} & \multicolumn{1}{c}{**Approach**} & \multicolumn{1}{c}{**Year**} & \multicolumn{1}{c}{**Ref.**} \\ \hline \(\surd\) & & EnKF + shrinkage/multiple regression & 2016 & [8] \\ \(\surd\) & & & nonparametric regression-based forecasting & 1998 & [9] \\ \(\surd\) & & & local polynomial regression-based load forecasting & 2001 & [10] \\ \(\surd\) & & & local linear regression & 2016 & [11] \\ \(\surd\) & & & a multi-equation time series model based on least squares & 2016 & [12] \\  & \(\surd\) & & K-means + CNN & 2017 & [13] \\  & \(\surd\) & & particle swarm + SVM & 2007 & [15] \\  & \(\surd\) & & simulated annealing particle swarm optimization + SVM & 2016 & [16] \\  & & \(\surd\) & LSTM & 2018 & [17] \\  & & \(\surd\) & ResNet + LSTM & 2018 & [18] \\  & & \(\surd\) & dual-attention mechanism + cross-optimization & 2022 & [20] \\  & & \(\surd\) & Lassopartial correlation analysis & 2018 & [21] \\  & & \(\surd\) & Fuzzy Cognitive Maps + Artificial Neural Networks & 2022 & [22] \\  & & \(\surd\) & - & 2022 & [23] \\  & & \(\surd\) & SVM + SNN + RNN + CNN & 2022 & [24] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review summary. of time and lacks the ability to learn on its own. The time series model requires a small amount of historical data, which effectively improves the work efficiency, but the method is not sensitive to climate, environment, and other influencing factors. The regression prediction model compared to the function statistical model accuracy and processing data speed has been improved, but there are still some problems. For example, [13] it is easy to fall into local optimal solutions [15; 16] which are only applicable to small datasets. Most of the existing deep learning algorithms are used for predicting power loads from non-renewable energy sources [17; 18; 20; 22], while there is not enough research related to predicting renewable energy power based on weather data. Refs. [23; 24] mainly considered the physical information factors related to PV power load prediction, which makes the prediction model only applicable to PV power prediction and the use of the model relatively limited. To address the aforementioned issues, this paper introduces a digital twin power forecasting platform for AI Grids. This platform utilizes digital twin technology to create a highly realistic simulation environment while accurately forecasting wind and PV (PV) power loads. The objective is to achieve precise monitoring, optimal control, and decision support for power system operation states. The main elements of this work are outlined below:  
(1) Construction of a digital twin visualization platform for AI Grids: this platform enables real-time monitoring of the grid's status, ensuring safe, reliable, and stable grid operation. (2) A wind energy and PV power load forecasting model called WPNet, based on deep learning, has been developed. This model is capable of predicting the power output of wind energy and PV power generation at specific future time points. It enhances the power forecasting accuracy from daily to hourly intervals, ensuring real-time performance in model predictions. (3) A data set for wind energy and photovoltaic power loads is constructed by using real data, and the effectiveness of the lightweight model for power load forecasting proposed in this paper is verified.