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基于深度学习的天然气居民客户用气量异常检测

Anomaly detectionof natural gas consumption for residential customers based on deep learning
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摘要 近年来,依托信息技术、人工智能等新兴技术,智慧燃气成为天然气行业的重要发展方向,促进了天然气行业快速发展和转型升级,也是新质生产力的体现。智能燃气表是智慧燃气的重要一环,其直接面向终端居民客户,通过监测其数据可以提高天然气行业的安全水平及经营管理效率,增强居民客户的满意度。针对天然气居民客户的用气量异常检测问题,提出将深度学习中的长短期记忆神经网络算法(LSTM)和传统的局部加权回归平滑法(LOWESS)相结合的异常检测模型,克服了传统异常检测模型建模时效率低下、无法给出异常值出现的具体时间等缺点。使用某天然气公司的生产数据验证了模型的实际效果,结果表明,异常检测模型效率更高,能有效地检测出用气量异常及异常值出现的时间点,为用气量异常检测提供了新的解决方案,有利于推动天然气行业新质生产力发展。 In recent years,smart gas has emerged as a significant trend in the natural gas industry,powered by information technology and artificial intelligence,accelerating industry’s rapid development and transformation and representing enhanced productivity.Smart gas meters,playing a pivotal role in the smart gas,directly serve residential customers and monitor gas consumption data,enhancing the industry’s safety and operational efficiency and boosting customer satisfaction.Aiming at the challenge of anomaly detection of natural gas consumption for residential customers,a novel anomaly detection model is proposed,integrating the Long Short-Term Memory(LSTM)algorithm from deep learning with the traditional Local Weighted Regression Smoothing(LOWESS)method.This model addresses the limitations of traditional anomaly detection methods,such as inefficiencies in modeling and an inability to pinpoint the exact time of outliers.The model’s effectiveness was validated using production data from a natural gas company,demonstrating its superior efficiency and capability in identifying anomalies of gas consumption and the precise moments of outliers.This innovative solution offers a new approach to anomaly detection of gas consumption,promoting the industry’s advancement in enhanced productivity.
作者 宫雨 李倩 曹馨 GONG Yu;LI Qian;CAO Xin(School of Economics and Management,China University of Petroleum,Beijing 102249,China)
出处 《煤炭经济研究》 2024年第7期27-34,共8页 Coal Economic Research
基金 国家自然科学基金资助项目(72274214,71904202) 中国石油大学(北京)科研基金资助项目(2462023YQTD002)
关键词 用气量异常检测 深度学习 智慧燃气 天然气居民客户 模型效果 anomaly detection of national gas comsumption deep learning smart gas natural gas residential customers model effect
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