基于智能算法与动态仿真的供热智能引擎构建
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摘要
智慧供热是基于区域供热系统的一种现代供热方式,是我国供热行业未来发展的重要方向。智能决策网作为智慧供热的核心系统,对实现智慧供热有着至关重要的作用。基于智能算法和动态仿真的有效融合,提出了构建供热系统智能引擎的技术路线。阐述了智能算法和动态仿真在智能决策网各核心功能模块(供热管网监测点优化布置、热负荷预测、水力仿真模型与模型参数校准、热动态模型、供热管网泄漏故障诊断)开发中的应用方法。该技术路线可以为智能决策网的研发提供理论指导。
Smart heating is a modern heating method based on district heating systems( DHSs) and an important direction for the future development of DHSs in China. As the cornerstone of smart heating,intelligent decision networks are crucial to achieve smart heating. Based on the effective integration of intelligent algorithms and dynamic simulation,a technical route to develop intelligence engines for DHSs is proposed. The application of intelligent algorithms and dynamic simulation in developing core modules of intelligent decision-making systems( optimization arrangement of monitoring points of heat-supply network,prediction of heat load,hydraulic simulation modeling and calibration of model parameters,thermal dynamic modeling and fault diagnosis of leakage of heat-supply network) is elaborated. This technical route can provide theoretical guidance for the development of intelligent decision networks.
Smart heating is a modern heating method based on district heating systems( DHSs) and an important direction for the future development of DHSs in China. As the cornerstone of smart heating,intelligent decision networks are crucial to achieve smart heating. Based on the effective integration of intelligent algorithms and dynamic simulation,a technical route to develop intelligence engines for DHSs is proposed. The application of intelligent algorithms and dynamic simulation in developing core modules of intelligent decision-making systems( optimization arrangement of monitoring points of heat-supply network,prediction of heat load,hydraulic simulation modeling and calibration of model parameters,thermal dynamic modeling and fault diagnosis of leakage of heat-supply network) is elaborated. This technical route can provide theoretical guidance for the development of intelligent decision networks.
引文
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[2]邹平华,方修睦,王芃,等.供热工程(下册,集中供热)[M].北京:中国建筑工业出版社,2018:263-264.
[3]钟崴,陆烁玮,刘荣.智慧供热的理念、技术与价值[J].区域供热,2018(2):1-5.
[4] GRON A. Hands-on machine learning with scikitlearn and tensorflow[M]. Sebastopol, California(US):O’Reilly Media,Inc.,2017:3-4.
[5]王晋达.基于遗传算法的供热管网阻力系数优化辨识研究(硕士学位论文)[D].哈尔滨:哈尔滨工业大学,2015.
[6] FANG T,LAHDELMA R. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system[J]. Applied Energy,2016,179:544-552.
[7] IDOWU S,SAGUNA S,HLUND C,et al. Applied machine learning:forecasting heat load in district heating system[J]. Energy and Buildings,2016,133:478-488.
[8] DOTZAUER E. Simple model for prediction of loads in district-heating systems[J]. Applied Energy,2002,73(3/4):277-284.
[9]于晓娟,顾吉浩,齐承英,等.几种集中供热负荷预测模型对比[J].暖通空调,2019,49(2):96-99.
[10] ALSHAMMARI E T,KEIVANI A,SHAMSHIRBAND S,et al. Prediction of heat load in district heating systems by support vector machine with firefly searching algorithm[J]. Energy,2016,95:266-273.
[11] SAJJADI S,SHAMSHIRBAND S,ALIZAMIR M,et al. Extreme learning machine for prediction of heat load in district heating systems[J]. Energy and Buildings,2016,122:222-227.
[12] GEYSEN D,DE SOMER O,JOHANSSON C,et al.Operational thermal load forecasting in district heating networks using machine learning and expert advice[J].Energy and Buildings,2018,162:144-153.
[13] SURYANARAYANA G,LAGO J,GEYSEN D,et al.Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods[J]. Energy,2018,157:141-149.
[14] RAHMAN A,SMITH A D. Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms[J]. Applied Energy,2018,228:108-121.
[15]周志刚.供热管网阻力特性的辨识研究(博士学位论文)[D].哈尔滨:哈尔滨工业大学,2006.
[16] ZHENG J,ZHOU Z,ZHAO J,et al. Integrated heat and power dispatch truly utilizing thermal inertia of district heating network for wind power integration[J]. Applied Energy,2018,211:865-874.
[17] ZHENG J,ZHOU Z,ZHAO J,et al. Effects of the operation regulation modes of district heating system on an integrated heat and power dispatch system for wind power integration[J]. Applied Energy,2018,230:1126-1139.
[18] LIANG W,ZHANG L,XU Q,et al. Gas pipeline leakage detection based on acoustic technology[J]. Engineering Failure Analysis,2013,31:1-7.
[19] REN L,JIANG T,JIA Z,et al. Pipeline corrosion and leakage monitoring based on the distributed optical fiber sensing technology[J]. Measurement,2018,122:57-65.
[20] ZHONG Y,XU Y,WANG X,et al. Pipeline leakage detection for district heating systems using multisource data in mid-and high-latitude regions[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2019,151:207-222.
[21]雷翠红.供热管网泄漏故障诊断的研究(博士学位论文)[D].哈尔滨:哈尔滨工业大学,2010:60-77.
[22] ZHOU S,ZHENG O,ONEILL C. A review of leakage detection methods for district heating networks[J]. Applied Thermal Engineering,2018,137:567-574.
[23]周志华.机器学习[M].北京:清华大学出版社,2016.