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.
Methods:
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.