A Study on Wind Power Prediction Based on Machine Learning Methods and Climate Character Analysis

Introduction: Wind energy is a kind of clean energy, and wind power generation is an important means of utilizing wind energy. However, wind power generation is highly volatile and prone to causing grid operation accidents, so it is necessary to predict wind power generation. The annual power generation of wind turbines is determined by the Turbine Power Curve (TPC), which, however, is easily affected by different meteorological conditions. The successful deployment of wind turbines requires accurate predictions of wind farm power before construction and near-real-time power predictions after construction to facilitate grid uptake. However, the existing research methods have not considered the quantitative influence of climate characteristics on the prediction accuracy of wind power, limiting the accuracy of wind power generation predictions. In order to solve this problem, this investigation applies a machine learning model that is based on decision trees to decrease the ambiguity associated with wind resource evaluations and to enhance the precision of wind energy forecasts. Method: The machine learning model, based on decision trees, was trained on four distinct classifications of vertical wind profiles to depict wind velocities necessitating complex computations across different rotor layer altitudes. Results: The findings indicated that the model after integration of rotor-equivalent wind speed and temperature lapse rate achieved a 21.48% enhancement in predictive accuracy for the dataset in question, surpassing traditional power curve techniques. Discussion: The model scrutinized the utility of incorporating parameters such as the wind speed at hub height, the wind speed equivalent to the rotor, and the rate of direct temperature decrease as variables for predicting power output. Climate feature data were also utilized to train the regression tree model, enabling the correlation of wind power with wind profile and climate features for predicting wind power based on physical relationships. Conclusion: This methodology has emerged as the optimal strategy for power forecasting across all categorized vertical wind profile types. Notably, the model incorporating the temperature lapse rate in the prediction exhibited higher accuracy than the other one, highlighting the significance of considering climatic characteristics in wind power prediction.