# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 1 Introduction  
With energy and environmental issues becoming more and more prominent [1], global energy is moving towards low-carbon, clean, safe, and efficient development [2]. In recent years, the wind energy industry has been developing rapidly around the world. According to statistics, the global newly installed capacity of wind power reached 93.6 GW in 2021. However, wind power generation is random and fluctuating due to climate conditions. Therefore, when large-scale wind power is integrated into the grid, it can jeopardize the safe and stable operation of the grid [3, 4]. Wind power prediction helps to optimize the grid integration of wind power, reduce operating costs, and promote the development of energy systems. Existing wind power prediction methods are categorized into ultra-short-term prediction [5, 6], short-term prediction [7, 8] and mid-to-long-term prediction [9, 10], with multiple prediction time dimensions. Commonly used wind power prediction models include physical [11, 12, 13, 14] and statistical [15, 16, 17, 18] methods. Physical methods utilize weather forecast data and related geographic information, and the models are generally more complex. Moreover, the weather forecast data will affect the prediction accuracy to a certain extent. They are often used for the mid-to-long-term prediction of new wind farms orwind farms with incomplete data. Statistical methods use learning algorithms to analyze historical data and establish the intrinsic connections between historical data. They are commonly used in short-term and ultra-short-term prediction. Of the many forecasting methods, deep learning [19] algorithms are the most widely used, mainly including time series methods [20] and artificial intelligence algorithms [21]. Common artificial intelligence algorithms include random forests [22], support vector machines [23], extreme learning machines [24], and artificial neural networks [25]. Long- and short-term memory neural networks [26; 27] are widely used in wind power prediction due to their excellent properties in analyzing time-series data. Convolutional neural networks (CNN) [28], which have achieved good performance in image processing, have also been applied to wind power prediction. [PERSON] et al. [29] proposed a combination of RANSAC (random sample consensus) noise screening and the Seq2 Seq-Attention-BiGRU model to enhance prediction accuracy. [PERSON] et al. [30] proposed a novel hybrid prediction method involving individual prediction model training, model ensemble, and error correction considering temporal correlation. [PERSON] et al. [31] proposed a variational mode decomposition-gate recurrent unit network prediction mode to enhance the accuracy of ultra-short wind power forecasting. Although these studies have achieved more accurate wind power prediction results, they lacked research on the spatial characteristics of the wind power data. Most wind power prediction studies predict the sum of the power of all turbines in the region, taking into account the temporal characteristics. However, the location and contextual information of wind turbines are neglected, and consideration of the variability of turbine power output at different spatial locations is lacking. The studies show that considering both temporal and spatial characteristics of wind turbine data can help improve the accuracy of wind power prediction. [PERSON] et al. [32] proposed a spatiotemporal wind power forecasting model, which utilized a GAT (graph attention network), GRU (gated recursion unit), and GAT-TCN (temporal convolutional network) as the main prediction methods. [PERSON] et al. [33] proposed a wind power prediction method based on spatiotemporal correlations considering the influences of wind speed, wind direction, and temperature. [PERSON] et al. [34] proposed a short-term wind power forecasting method based on feature clustering and correlation analysis, improving forecasting accuracy through data feature clustering, variable correlation analysis, and building forecasting models.