城镇天然气负荷预测方法研究进展  

Progress of research on load forecasting method for city gas

作　　者：徐鹏 杜景勃 刘伟 XU Peng;DU Jingbo;LIU Wei(School of Environmental and Energy Engineering,Beijing University of Civil Engineering and Architecture//Beijing Key Laboratory of Heating,Gas Supply,Ventilation and Air Conditioning Engineering;Beijing Gas and Heating Engineering Design Institute)

机构地区：[1]北京建筑大学环境与能源工程学院·供热、供燃气、通风及空调工程北京市重点实验室 [2]北京市煤气热力工程设计院有限公司

出　　处：《油气储运》2023年第5期481-491,共11页Oil & Gas Storage and Transportation

基　　金：内蒙古自治区科技重大专项“中低压纯氢与掺氢燃气管道输送及其应用关键技术研发”,2021ZD0038。

摘　　要：合理、准确地预测城镇天然气负荷已成为政府制订政策、气源端与管网建设以及优化运行调度的重要保障。在对国内外相关文献和实践进行广泛调研的基础上,结合天然气工业的发展历程,系统梳理了城镇天然气负荷预测技术研究进展,将其分类为早期传统预测方法、基于机器学习的预测方法以及组合预测方法。中国在应用基于机器学习的智能算法预测天然气负荷的研究方面已实现跨越式发展,成为当前国际上最活跃的研究群体。组合预测方法细化了对天然气用气特征的分析,预测结果的准确性更高,各种预测方法的组合应用研究已成为天然气负荷预测研究的热点。未来,应注重大数据处理和先进算法技术在天然气负荷预测中的应用,但仍不能忽视对负荷形成机制、特点等技术本质的深入研究。(图1,表1,参87)Reasonable and accurate forecast of city gas load has become an important guarantee for government policy formulation,gas source terminal and pipeline network construction,as well as the optimized operation and scheduling of natural gas.Based on extensive research on relevant literature and practice at home and abroad,the research progress of city gas load forecasting technology was systematically reviewed in the light of the development history of the natural gas industry.The forecasting methods were divided into the early traditional forecasting methods,machine learning-based forecasting methods and combined forecasting methods.The research on applying intelligent algorithms based on machine learning to forecast natural gas loads has achieved the leapfrog development in China and thus the research group becomes the most active internationally at the current stage.The combined forecasting methods have refined the analysis on natural gas consumption characteristics and allow for more accurate forecasting results.Hence,the research on the combined application of forecasting methods has become a hot topic in current gas load forecasting research.In the future,more attention should be paid to the application of big data processing and advanced algorithm technology in gas load forecasting,but the in-depth exploration of the technical essence,such as the load formation mechanism and characteristics,should not be neglected.(1 Figure,1 Table,87 References).

关 键 词：天然气 城镇燃气 负荷预测 机器学习 组合预测 

分 类 号：TE832[石油与天然气工程—油气储运工程]