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基于时间序列方法的集中供热系统负荷预测与动态调节研究
Alternative TitleResearch of Central Heating System Load Forecasting and Dynamic Regulation Based on Time Series Method
魏延宝
Subtype工学硕士
Thesis Advisor林红权
2014-05-29
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline控制理论与控制工程
Keyword集中供热 负荷预测 Arimax模型 时间序列方法 动态调节 Central Heating System Load Forecasting Arimax Model Time Series Method Dynamic Regulation
Abstract近年来,集中供热系统因其高效节能等优势逐渐成为城市采暖供热的首选,如何实现按需供热和经济运行是集中供热系统运行调节过程中亟待解决的关键问题。传统的调节方式以室外温度为参考,由操作人员凭经验进行供热参数的确定和调节。这种主观粗略的调节方式缺乏理论依据,很难达到按需供热的目的;同时,由于供热系统存在很大的热惯性,这种滞后的调节方式难以保证供热质量。 本文针对集中供热运行调节存在的不足,提出基于时间序列分析的热负荷预测及动态调节方法。通过对系统热特性研究,利用供热参数预测方法很好地解决了热源调度及换热站调节过程中存在的问题,使供热系统处于高效、经济的运行状态。 首先,本文通过对供热系统历史数据的分析研究,建立了带室外温度的热负荷二元ARIMAX模型。作为对比,利用相同数据分别建立了热负荷ARIMA模型以及RBF神经网络模型。将三种模型24小时热负荷预测结果与实际热负荷进行比较,证明ARIMAX模型在进行热负荷预测时具有更高的精度。利用负荷预测的结果,本文讨论了带调峰锅炉的供热系统负荷分配方法。利用KKT条件,解决热源多台锅炉的热负荷优化分配问题,在保证供热总量的前提下减小燃料损耗。 针对换热站二次热网,本文采用先动态确定二次侧供、回水平均温度,再以此为设定值控制一次侧阀门的动态调节方式。利用时间序列分析方法,建立以室外温度和供、回水平均温度为输入项的用户室内温度三元ARIMAX模型。通过对三元模型的逆向推导获得平均温度动态模型,即可根据室内温度的目标值以及温度历史数据确定二次侧供、回水平均温度的设定值。利用动态模型一步递推,进行换热站二次热网动态调节,保证用户室内温度满足供暖要求。 最后,本文对集中供热系统负荷预测及动态调节监控管理软件进行了初步设计。监控管理软件作为负荷预测以及动态调节方法的实现与应用平台,为操作人员运行调节提供数据辅助和决策支持,对提高供热质量、节约能源具有重要意义。
Other AbstractThe central heating system has gradually become the first choice of the urban heating because of its advantages such as high efficiency and energy saving. How to realize the on-demand heating and economic operation is now the key problem to be solved in the process of the adjustment to the heating system. Traditionally, the operating personnel determine and adjust the parameters by experience referring to the outdoor temperature. This subjective and rough way of regulation is lack of theoretical basis and difficult to achieve the purpose of on-demand heating; at the same time, due to a great thermal inertia, this lagging method of regulation can’t guarantee the quality of central heating. Therefore, accurate adjustment needs scientific and reasonable method through studying the characteristics and laws of the heating system. Considering the shortcomings in the process of regulation, heat load forecasting and dynamic adjustment was proposed in this paper based on time series analysis method. This method could solve the problems about heat source dispatch and regulation of heat exchange station, to ensure the efficient and economic operation through characteristic research and parameter prediction of the heating system. First, through the analysis and study of the historical data, this article established a two-variable ARIMAX model of the heat load with outdoor temperature as the input.As comparison, a one-variable ARIMA model and a RBF neural network model were buildt based on the same data. Comparing between 24-hour forecasting results of the three models and actual heat load, the results proved higher precision of the ARIMAX model in solving the problem of heat load forecasting. Based on the load forecasting results, this article discussed the heating system load distribution of central heating system with peak-shaving boiler. According to the KKT conditions, it was able to realize heat load optimized distribution and reduce fuel consumption based on the total heating input. In view of secondary heat supply network regulation, this paper first dynamically determined the average supply and return water temperature, then adjust dynamically by controlling the valve in first heat network. Using time series analysis method, a three variable ARIMAX model of user indoor temperature was established with two inputs: outdoor temperature as well as the average supply and return water temperature. Through reverse deduction of the prediction model,a dynamic mode...
shelfnumXWLW2046
Other Identifier201128014628018
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7725
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
魏延宝. 基于时间序列方法的集中供热系统负荷预测与动态调节研究[D]. 中国科学院自动化研究所. 中国科学院大学,2014.
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