基于自适应SA-PSO改进的XGBoost气温预测方法

Improved XGBoost temperature prediction method based on SA-PSO

提出了一种SA-PSO-XGBoost预测模型,基于2016年1月1日~2017年12月31日的ECMWF气象数据,用于预测南京地区6 h后的气温。将气象数据分为训练集和测试集,使用PCA降维方法对气象数据特征进行压缩降维,然后应用模拟退火和粒子群优化混合算法对XGBoost模型的超参数进行优化,并将测试集数据带入到SA-PSO-XGBoost、XGBoost、GRU和LSTM神经网络进行对比分析,实验结果表明:SA-PSO-XGBoost预测6 h后的温度模型有更高的准确性和鲁棒性。

Proposes a SA-PSO-XGBoost prediction model for forecasting the temperature in Nanjing,based on ECMWF meteorological data from January 1,2016,to December 31,2017.The meteorological data is divided into training and testing sets.The PCA dimension reduction method is applied to compress and reduce the features of the meteorological data.The SA-PSO-XGBoost model optimizes the hyperparameters using a hybrid algorithm combining simulated annealing and particle swarm optimization.The testing set is then used to compare the performance of the SA-PSO-XGBoost model with XGBoost,GRU,and LSTM neural networks.Experimental results demonstrate that the SA-PSO-XGBoost model outperforms others in terms of accuracy and robustness in predicting the temperature 6 hours ahead.