# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 1 Introduction  
The large-scale development and utilization of new energy is conducive to the smooth realization of low-carbon goals. According to the \"bp Statistical Review of World Energy 2022\" [1], benefiting from the strong growth of wind and solar energy, renewable energy accounts for 12.8% of the total global energy use in 2021, surpassing nuclear energy, which accounts for 9.8%; renewable energy power generation increased by 16.5% compared to the previous year, while coal power generation increased by only 8.5%, and natural gas power generation increased by only 2.3%. In the process of promoting the development and utilization of unstable energy such as wind energy and solar energy, the imbalance of supply and demand caused by its intermittent nature is a major threat to the safe and stable operation of the power transmission and transformation network [2]. In order to improve the energy network's ability to absorb renewable energy, a feasible method is to predict and evaluate uncertain renewable energy, and then improve the resilience of the energy system through joint complementary means such as storage conversion [3], demand-side management [4], and co-ordination of electric vehicle charging needs [5]. The reasonable planning of equipment capacity parameters and formulation of production scheduling plans on the basis of prediction and evaluation can further reduce operating costs. According to different time scales, the current forecasting methods can be divided into ultra-short-term forecasting [6; 7], short-term forecasting [8], medium-term, and long-term forecasting [9; 10]. The prediction method models mainly include the physical model [11; 12], statistical model [13], grey model [14; 15], and machine learning [16; 17]. Among them, the physical model builds and solves the atmospheric flow equations; the statistical model uses differential equations to solve the trend of data correlation; and the artificial intelligence method [18] which is good at dealing with multi-dimensional nonlinear problems is increasingly being used to break through computational bottlenecks of traditional methods. Improving forecast accuracy is one of the research focuses on issues related to renewable energy forecasting. Data feature enhancement and model algorithm optimization are currently widely used ideas. In the data pre-processing analysis stage, variable correlations or priorities are calculated to extract relevant feature parameters [19; 20]. Periodic fluctuation features are then separated by data stacking [21] or modal decomposition [22; 23; 24] or discrete wavelet transform [25], which can achieve the goal of improving prediction accuracy. Deep learning or neural networks are widely chosen for building novel predictive models with complex structures. For example, a model combined with a graph-structured neural network [26] can better identify the spatial correlation of data, and a model based on a long-short-term memory neural network [27] can learn the temporal correlation of data well. As the structure of the emerging model becomes complex and diverse, a large number of internal parameters are globally adjusted by the excellent optimizer [28; 29; 30] to achieve the overall best working performance. On the premise of ensuring the accuracy of the prediction, the calculation amount of the network parameter optimization can be reduced by means of an optimized weight calculation [31] and attention mechanism [6; 32], so that the model calculation performance can be improved. It is worth noting that the development of artificial intelligence has brought new ideas to the field of energy research. Machine learning is a powerful tool for data mining that can be flexibly extended and combined to perform data-related tasks, and wind power prediction based on it has received a great deal of attention in the energy field. In this study, machine learning was used in order to achieve accurate wind energy prediction.