Analysis of the effect of meteorological elements on new energy power prediction based on machine learning

Abstract

Background: With the gradual construction of new power systems, new energy sources, such as wind and photovoltaic power, will gradually dominate positions in the power supply structure, directly leading the new power system to rely heavily on accurate meteorological forecasts. High-precision and high-resolution meteorological forecasts are important technical methods to improve the safe, stable, and economic operation of the new power system. Objective: Since the analysis of meteorological elements is the basis of meteorological forecasting, in this paper, the effect of different meteorological elements including temperature, relative humidity, air pressure, wind speed, wind direction, and radiation on the performance of power forecasting, was analyzed by using 7 machine learning algorithms in 5 provinces in southern China. Method: About 5 provinces in southern China were selected as the research objects, and 7 typical machine learning algorithms were applied and compared, including support vector machine (SVM), decision tree (DT), random forest (RFR), K-nearest neighbor (KNN), Linear Regression (LR), Ridge Regression (RR), and Lasso Regression (Lasso R). At the same time, the influence of different meteorological elements, such as temperature, relative humidity, air pressure, wind speed, wind direction, and radiation amount, on the prediction performance of wind power and photovoltaic power was considered. Then, the performance of different regression models was further investigated and analyzed. Results: Based on the data of 10 new energy stations in 5 regions, the research on the prediction performance of 7 machine learning methods shows that the performance of models in different regions varies greatly. Among the 10 selected new energy stations, the RFR model and KNR model have superior overall performance. Conclusion: This study shows how variable importance and prediction accuracy depend on regression methods and climatic variables, providing effective methods to assess the interdependence of meteorological variables and the importance of meteorological variables in predicting output power.