郑州大学学报（理学版）2026，Vol.58Issue(5)：30-36,7.DOI:10.13705/j.issn.1671-6841.2024194
基于XGBoost-FOA-LSTM的燃气分类负荷预测
Gas Classification Load Prediction Based on XGBoost-FOA-LSTM
摘要
Abstract
With accurate gas forecasting can be effectively adjusted supply and can be reduced unneces-sary energy consumption.The natural gas load of cities and residents was influenced by a variety of fac-tors,including temperature,heating conditions,weather conditions,wind,air quality,and holiday.To address the issue that existing natural gas load forecasting methods were mostly limited to a single forecas-ting period and user type,a weekly load forecasting method based on the XGBoost-FOA-LSTM model was proposed.The prediction results of XGBoost served as a new influencing factor for the rolling prediction of LSTM,to forecast the load of different users with different influencing factors and at different times.The validation results showed that the model had prediction errors of 3.65%and 6.16%in rolling daily fore-casting for city and residential data,respectively.Its root mean square error(RMSE)and mean absolute error(MAE)were both superior to those of the single LSTM model,further proving that the daily load data of cities were significantly affected by heating conditions.关键词
燃气预测/XGBoost/FOA/LSTMKey words
gas prediction/XGBoost/FOA/LSTM分类
能源科技引用本文复制引用
宰光军,王浩,武杨磊,李怡彬,刘燕..基于XGBoost-FOA-LSTM的燃气分类负荷预测[J].郑州大学学报（理学版）,2026,58(5):30-36,7.基金项目
郑州大学产学研用科技创新团队培育项目(32214110-23) （32214110-23）
