西南石油大学学报（自然科学版）2026，Vol.48Issue(2)：107-124,18.DOI:10.11885/j.issn.1674-5086.2024.04.01.04
不同时间尺度的燃气负荷预测模型研究综述
A Review of Researches on Gas Load Forecasting Models Across Different Time Scales
赵春兰 1郑雯娟 2岑康 3贺可函 2王汉遥2
作者信息
- 1. 西南石油大学理学院,四川 成都 610500||能源安全与低碳发展重点实验室,四川 成都 610500
- 2. 西南石油大学理学院,四川 成都 610500
- 3. 西南石油大学土木工程与测绘学院,四川 成都 610500
- 折叠
摘要
Abstract
Accurate prediction of gas load holds significant practical value for maintaining the dynamic balance between gas supply and demand.With advancements in artificial intelligence technology,gas load forecasting algorithms have undergone substantial development.This study first categorizes forecasting periods into three distinct phases:short-term(ST),medium-to-short-term(MST),and medium-to-long-term(MLT).From an algorithmic perspective,we systematically analyze six represen-tative methods including eXtreme Gradient Boosting(XGBoost)for ST forecasting,two approaches such as Long Short-Term Memory(LSTM)networks for MST forecasting,and two techniques including Prophet for MLT forecasting,and evaluate their advantages,limitations,and application scenarios.Through empirical validation using operational data,we conduct multi-dimensional comparative analysis of 12 selected models.Our experimental framework encompasses critical aspects including dataset construction,data preprocessing,extrapolative prediction,parameter optimization,and model evaluation across differ-ent temporal scales.Finally,we propose forward-looking perspectives on future research directions in gas load forecasting,particularly focusing on practical applications in natural gas dispatch management.This comprehensive investigation provides valuable references for advancing algorithmic research in gas load prediction and its implementation in smart energy manage-ment systems.关键词
燃气负荷预测/短期预测/中短期预测/中长期预测/机器学习/深度学习/综述Key words
gas load forecasting/short-term forecasting/medium-to-short-term forecasting/medium-to-long-term forecasting/machine learning/deep learning/review分类
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赵春兰,郑雯娟,岑康,贺可函,王汉遥..不同时间尺度的燃气负荷预测模型研究综述[J].西南石油大学学报（自然科学版）,2026,48(2):107-124,18.
