基于乘积季节性ARIMA模型负荷预报及节能监控软件研究
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摘要
集中供热逐渐成为城市尤其是北方城市建设基础之一,如何使整个集中供热系统处于一个良好的、高效的运行状态,成为供热控制系统所必须解决的问题。但供热负荷具有一定的随机性,传统的供热系统的运行仅仅是以天气情况为依据,调度人员往往根据天气情况和工作经验来调节热网运行参数,因而热网调度的精确性较差,所以对供热负荷进行预报是至关重要的。本文采用乘积季节性ARIMA模型与以往其他的调度方法进行比较,利用采集得到的供热系统的实时数据来预报下一个采样时刻的预报值,并以此为依据用于供热系统的优化调度,将对供热系统的节能运行和实时控制起到重要作用。
首先对供热负荷原始数据进行预处理,将平稳时间序列作为分析的对象。接着分别利用AR横向、纵向、交叉负荷预报,乘积季节性ARIMA模型方法实现供热负荷预报,结果表明应用乘积季节性ARIMA模型进行预报的效果良好,且优于以前的预报算法。同时本文所采用的乘积季节性ARIMA模型能够进行自动完成阶数选取,使模型达到最优。
论文最后将乘积季节性ARIMA方法应用于集中供热系统智能管理软件的预报调度中。系统设计分为两大部分:负荷预报和监控管理。其中监控管理主要包括数据处理系统、用户管理系统、查询系统、输入输出系统、安全报警系统;负荷预报包括预测控制系统。利用热负荷原始数据进行预报,监控管理界面将根据负荷预报值给出诸多调整建议,使操作人员可以更为直观的监控调整供热系统,指导供热网络节能运行。对于提高供热质量和节约能源都有着重要的意义。
Central heating has gradually become the city one of the foundations of the northern cities. How to make the whole central heating system in a good and efficient operation become more and more necessary. However, heating load with a certain degree of randomness, the traditional operation of heating system is only based on weather conditions and work experience to regulate the heat supply network operating parameters, thus the accuracy of it is less. Therefore, on the heating load forecasting is essential. In this paper, the product of the multiple seasonal ARIMA model with the previous comparison of the scheduling method, uses the heating system has been collecting real-time data to forecast the next sampling time values. Using it as the basis for optimal scheduling of heating system, energy-efficient heating system will run and play an important role in real-time control.
First of all, the dissertation adopts the original data collected in heating supply station and makes data pre-processing. Secondly, the use of horizontal, vertical, cross-load forecasting, multiple seasonal ARIMA model of the product heat load forecasting method, results showed that the application of the product of the multiple seasonal ARIMA model forecasting the effects of good and better than the previous prediction algorithm. At the same time, the multiple seasonal ARIMA model can automatically complete the selection order.
Finally, the multiple seasonal ARIMA method has been applied to central heating system. The intelligent management software includes two parts; these are monitoring system and forecasting system, and monitoring system includes data processing systems, user management systems, query systems, input-output systems, security system. Monitoring and management interface will be given a lot of load forecasting value of the proposed revision, so that operators can monitor more intuitive adjustment of heating system, and guide heating system to save energy, and these are of great significance for improving the quality and energy supply.
首先对供热负荷原始数据进行预处理,将平稳时间序列作为分析的对象。接着分别利用AR横向、纵向、交叉负荷预报,乘积季节性ARIMA模型方法实现供热负荷预报,结果表明应用乘积季节性ARIMA模型进行预报的效果良好,且优于以前的预报算法。同时本文所采用的乘积季节性ARIMA模型能够进行自动完成阶数选取,使模型达到最优。
论文最后将乘积季节性ARIMA方法应用于集中供热系统智能管理软件的预报调度中。系统设计分为两大部分:负荷预报和监控管理。其中监控管理主要包括数据处理系统、用户管理系统、查询系统、输入输出系统、安全报警系统;负荷预报包括预测控制系统。利用热负荷原始数据进行预报,监控管理界面将根据负荷预报值给出诸多调整建议,使操作人员可以更为直观的监控调整供热系统,指导供热网络节能运行。对于提高供热质量和节约能源都有着重要的意义。
Central heating has gradually become the city one of the foundations of the northern cities. How to make the whole central heating system in a good and efficient operation become more and more necessary. However, heating load with a certain degree of randomness, the traditional operation of heating system is only based on weather conditions and work experience to regulate the heat supply network operating parameters, thus the accuracy of it is less. Therefore, on the heating load forecasting is essential. In this paper, the product of the multiple seasonal ARIMA model with the previous comparison of the scheduling method, uses the heating system has been collecting real-time data to forecast the next sampling time values. Using it as the basis for optimal scheduling of heating system, energy-efficient heating system will run and play an important role in real-time control.
First of all, the dissertation adopts the original data collected in heating supply station and makes data pre-processing. Secondly, the use of horizontal, vertical, cross-load forecasting, multiple seasonal ARIMA model of the product heat load forecasting method, results showed that the application of the product of the multiple seasonal ARIMA model forecasting the effects of good and better than the previous prediction algorithm. At the same time, the multiple seasonal ARIMA model can automatically complete the selection order.
Finally, the multiple seasonal ARIMA method has been applied to central heating system. The intelligent management software includes two parts; these are monitoring system and forecasting system, and monitoring system includes data processing systems, user management systems, query systems, input-output systems, security system. Monitoring and management interface will be given a lot of load forecasting value of the proposed revision, so that operators can monitor more intuitive adjustment of heating system, and guide heating system to save energy, and these are of great significance for improving the quality and energy supply.
引文
1黄文,管昌生.城市集中供热研究现状及发展趋势.国外建材科技. 2004,25(5): 78~80
2 Molodezhnikova.L.I, KonishevA.E, Yusupova Yu.B. Problem solving search for energy saving in heating network. Proceedings of the 7th international scientific and practical conference of students. Tomsk. 2001:327~332
3 Fredrik.Werntedt, Paul Davidsson. An agent-based approach to monitoring and control of district systems. Application of artificial intelligence and expert systems. Australia, 2002, 1(2):402~411
4 Bojic.M, Trifunovic, Gustafsson. Mixed 0-1 Sequential Linear programming optimization of heat distribution in a district-heating system. Energy and building. 2000,32(3):309~317
5邹平华.借鉴俄罗斯经验积极发展我国集中供热事业.暖通空调. 2000, 30(4):33~37
6韦新东,尹军,全贞花.日本集中供热(冷)系统的发展现状.吉林建筑工程学院学报. 2001. 1(3):28~30
7杨治金.丹麦的集中供热.区域供热. 2000, 1(6):27~30
8 Halzl.Jozsef. Reduction of energy costs with cogeneration for district heating System. Official Proceedings-Annual Conference of the International District Heating Association. 2001, 2(76):398~412
9刘贺明.中国城市集中供热发展与改革情况.区域供热. 2003, 1(3):4~8
10董壮进,廖荣平.供热管网初调节的探讨.煤气与动力. 2000,1(7):270~272
11王耕,王祈毅.城市集中供热网智能协调控制系统.测控技术, 2000, 1(10):24~26
12孟哲,刘应宗,金宇澄.我国集中供热的现状与对策.华东交通大学学报. 2004, 21(3):66~69
13付林,江亿,张寅平.采暖供热系统的应用浅析.热能与动力工程. 2000, 15(5):459~463
14贺平.论我国集中供热技术发展趋向的几个主要问题.区域供热. 1992, 1(3):6~11
15王享麟.当前集中供热中的热点问题.中国建设信息. 2001, 1(25):50~53
16石亿.我国建筑耗能状况及有效的节能途径.暖通空调. 2005, 35(5):30~40
17李先瑞,郎四维.我国建筑供热采暖的现状及问题分析.区域供热. 2001, 1(3):11~22
18李天智,孙海波.城市集中供热系统控制测流.河北省科学院学报. 2000, 4(1):205~207
19 Gudmundur.R.Josson, Valdimar.K.Jonsson. Equivalent outdoor temperature for district heating systems. A share trans. 1995, 101(1):394~399
20 Zhijian Hou, Zhiwei Lian Phd, Ye Yao Phd. Cooling load prediction based on the combination of rough set theory and support vector machine. HVAC&R Research. 2006, 12(2):337~352
21 Dotzauer E. Simple model for prediction of lead in district-heating system.Applied energy. 2002, 73(3):277~284
22 Philip.B. Makeup Air heat recovery saving energy in labs. ASHRAEJ. 2004, 46(2):35~40
23 K.J.Hunt. Neural networks for control systems -A survey.Automatica.2002, 2(5):1083~1112
24 Isogai, F. Ara, T. Fukuda. Modeling and vibration control with neural network for flexible multi-link structure Proceedings. 1999 IEEE International Conference on Robotics and Automation. 1999, 1(2):1096~1100
25 Yang, D.M.Vilathgamuwa, M.A. Rahman. Implementation of anartificial neuralnetwork-based real-time adaptive controller for an interior permanent magnet motor drive. IEEE Trans on Industry Applications. 2003, 39(1): 96~104
26吴长山.指数平滑法组合预报方法与应用.松辽学刊. 1998, 10(4): 62~64
27 Weigend. Time series analysis and prediction using gated experts with application to energy forecasts. Applied Artificial Intelligence. 1996,10(6):583~624
28冯利华.灰色预报模型的问题讨论.系统工程理论与实践. 2001, 12(17):125~128
29周恩泽,方修睦.供暖热水锅炉房的热负荷预报.哈尔滨建筑大学学报.2000, 33(3):56~60
30郝有志,李德英.热负荷预报方法评析.热能通风空调. 2003, 2(1):26~27
31孟繁威.基于粒子群算法热力站供热负荷预测与控制研究.大连理工学, 2006, 2(3):2~4
32高捷,樊洪斌.热电厂热网计算机管理的现状和发展.区域供热. 2001, 2(1):6~8
33 GaoQi-Sheng. Study on statistical quality control based on time series models Proceedings. Chinese Control and Decision Conference. 2004, 2(11):694~696
34 Yourstone, Steven A. Time-series approach to discrete real-time process quality Control. Quality and Reliability Engineering International. 1999, 5(4):309~317
35张数京,齐立心.时间序列分析简明教程.北京:清华大学出版社, 2003
36齐维贵,朱学莉.时间序列预报法在供热控制中的应用.研究电子学报.2002, 31(2):267~270
37 Chen JF, Wang WM, Huang CM. Analysis of an adaptive time series auto regressive moving average model for short-term load forecasting. Electric Power System Research. 1995, 1(34):187~196
38赵海青,牛东晓.负荷预报的交叉式自适应优选组合预报模型.华北电力大学学报. 2000, 27(4):1317
39 S. M.潘迪特,吴宪民.时间序列及系统分析与应用.北京:机械工业出版社,1988
40吴怀宇.时间序列分析与综合.武汉:武汉大学出版社, 2004
41安德宏,柳湘月.基于季节性ARIMA模型的电力负荷建模与预报.天津大学学报. 2004, 2(37):187~189
42 Box.G, Jenkins.G,Reinsel.C. Time Series Analysis Forecasting and Control. Englewood Cliffs. 1994, 2(12):12~14
43 Minoru Kawashima, Charies E Dorgan, John W Mitchell. Hourly thermal load prediction for the next 24 hours by ARIMA, EWMA, LR and an artificial neural network. A share Trans. 1995, 101(1):186~200
44 Anshuman.Gupta, Maranas C D. Managing demand uncertainty in supply chain planning. Computers and Chemical Engineering. 2003, 27(8):1219~1227
45高仁祥马超群.摩托车市场需求预测模型研究.中国管理科学. 1997, 5(2):32-35
46杨桂元.组合预测方法在季节波动预测中的应用.江苏统计, 1999, 3(1):12~14
47纪跃之,利用季节指数修正指数平滑预测值对公路客运量进行预测. 1007,13(2):112~120
48申刚,徐光虎.电力系统中长期负荷预测软件包的开发.电网技术, 2002, 26(5):14~17
49 YU Xuan, CHENG Hao-zhong, YOU Shi-hong, Development and application of power system mid-long term load forecasting software package. Proceedings of the EPSA, 2004, 16(2):9~13
50 WANG Xiao-tong. Application of power load forecasting software Hefei. Automation of electric power system. 2004, 28(20):94~96
2 Molodezhnikova.L.I, KonishevA.E, Yusupova Yu.B. Problem solving search for energy saving in heating network. Proceedings of the 7th international scientific and practical conference of students. Tomsk. 2001:327~332
3 Fredrik.Werntedt, Paul Davidsson. An agent-based approach to monitoring and control of district systems. Application of artificial intelligence and expert systems. Australia, 2002, 1(2):402~411
4 Bojic.M, Trifunovic, Gustafsson. Mixed 0-1 Sequential Linear programming optimization of heat distribution in a district-heating system. Energy and building. 2000,32(3):309~317
5邹平华.借鉴俄罗斯经验积极发展我国集中供热事业.暖通空调. 2000, 30(4):33~37
6韦新东,尹军,全贞花.日本集中供热(冷)系统的发展现状.吉林建筑工程学院学报. 2001. 1(3):28~30
7杨治金.丹麦的集中供热.区域供热. 2000, 1(6):27~30
8 Halzl.Jozsef. Reduction of energy costs with cogeneration for district heating System. Official Proceedings-Annual Conference of the International District Heating Association. 2001, 2(76):398~412
9刘贺明.中国城市集中供热发展与改革情况.区域供热. 2003, 1(3):4~8
10董壮进,廖荣平.供热管网初调节的探讨.煤气与动力. 2000,1(7):270~272
11王耕,王祈毅.城市集中供热网智能协调控制系统.测控技术, 2000, 1(10):24~26
12孟哲,刘应宗,金宇澄.我国集中供热的现状与对策.华东交通大学学报. 2004, 21(3):66~69
13付林,江亿,张寅平.采暖供热系统的应用浅析.热能与动力工程. 2000, 15(5):459~463
14贺平.论我国集中供热技术发展趋向的几个主要问题.区域供热. 1992, 1(3):6~11
15王享麟.当前集中供热中的热点问题.中国建设信息. 2001, 1(25):50~53
16石亿.我国建筑耗能状况及有效的节能途径.暖通空调. 2005, 35(5):30~40
17李先瑞,郎四维.我国建筑供热采暖的现状及问题分析.区域供热. 2001, 1(3):11~22
18李天智,孙海波.城市集中供热系统控制测流.河北省科学院学报. 2000, 4(1):205~207
19 Gudmundur.R.Josson, Valdimar.K.Jonsson. Equivalent outdoor temperature for district heating systems. A share trans. 1995, 101(1):394~399
20 Zhijian Hou, Zhiwei Lian Phd, Ye Yao Phd. Cooling load prediction based on the combination of rough set theory and support vector machine. HVAC&R Research. 2006, 12(2):337~352
21 Dotzauer E. Simple model for prediction of lead in district-heating system.Applied energy. 2002, 73(3):277~284
22 Philip.B. Makeup Air heat recovery saving energy in labs. ASHRAEJ. 2004, 46(2):35~40
23 K.J.Hunt. Neural networks for control systems -A survey.Automatica.2002, 2(5):1083~1112
24 Isogai, F. Ara, T. Fukuda. Modeling and vibration control with neural network for flexible multi-link structure Proceedings. 1999 IEEE International Conference on Robotics and Automation. 1999, 1(2):1096~1100
25 Yang, D.M.Vilathgamuwa, M.A. Rahman. Implementation of anartificial neuralnetwork-based real-time adaptive controller for an interior permanent magnet motor drive. IEEE Trans on Industry Applications. 2003, 39(1): 96~104
26吴长山.指数平滑法组合预报方法与应用.松辽学刊. 1998, 10(4): 62~64
27 Weigend. Time series analysis and prediction using gated experts with application to energy forecasts. Applied Artificial Intelligence. 1996,10(6):583~624
28冯利华.灰色预报模型的问题讨论.系统工程理论与实践. 2001, 12(17):125~128
29周恩泽,方修睦.供暖热水锅炉房的热负荷预报.哈尔滨建筑大学学报.2000, 33(3):56~60
30郝有志,李德英.热负荷预报方法评析.热能通风空调. 2003, 2(1):26~27
31孟繁威.基于粒子群算法热力站供热负荷预测与控制研究.大连理工学, 2006, 2(3):2~4
32高捷,樊洪斌.热电厂热网计算机管理的现状和发展.区域供热. 2001, 2(1):6~8
33 GaoQi-Sheng. Study on statistical quality control based on time series models Proceedings. Chinese Control and Decision Conference. 2004, 2(11):694~696
34 Yourstone, Steven A. Time-series approach to discrete real-time process quality Control. Quality and Reliability Engineering International. 1999, 5(4):309~317
35张数京,齐立心.时间序列分析简明教程.北京:清华大学出版社, 2003
36齐维贵,朱学莉.时间序列预报法在供热控制中的应用.研究电子学报.2002, 31(2):267~270
37 Chen JF, Wang WM, Huang CM. Analysis of an adaptive time series auto regressive moving average model for short-term load forecasting. Electric Power System Research. 1995, 1(34):187~196
38赵海青,牛东晓.负荷预报的交叉式自适应优选组合预报模型.华北电力大学学报. 2000, 27(4):1317
39 S. M.潘迪特,吴宪民.时间序列及系统分析与应用.北京:机械工业出版社,1988
40吴怀宇.时间序列分析与综合.武汉:武汉大学出版社, 2004
41安德宏,柳湘月.基于季节性ARIMA模型的电力负荷建模与预报.天津大学学报. 2004, 2(37):187~189
42 Box.G, Jenkins.G,Reinsel.C. Time Series Analysis Forecasting and Control. Englewood Cliffs. 1994, 2(12):12~14
43 Minoru Kawashima, Charies E Dorgan, John W Mitchell. Hourly thermal load prediction for the next 24 hours by ARIMA, EWMA, LR and an artificial neural network. A share Trans. 1995, 101(1):186~200
44 Anshuman.Gupta, Maranas C D. Managing demand uncertainty in supply chain planning. Computers and Chemical Engineering. 2003, 27(8):1219~1227
45高仁祥马超群.摩托车市场需求预测模型研究.中国管理科学. 1997, 5(2):32-35
46杨桂元.组合预测方法在季节波动预测中的应用.江苏统计, 1999, 3(1):12~14
47纪跃之,利用季节指数修正指数平滑预测值对公路客运量进行预测. 1007,13(2):112~120
48申刚,徐光虎.电力系统中长期负荷预测软件包的开发.电网技术, 2002, 26(5):14~17
49 YU Xuan, CHENG Hao-zhong, YOU Shi-hong, Development and application of power system mid-long term load forecasting software package. Proceedings of the EPSA, 2004, 16(2):9~13
50 WANG Xiao-tong. Application of power load forecasting software Hefei. Automation of electric power system. 2004, 28(20):94~96