集中供熱系統(tǒng)動態(tài)負荷預測與節(jié)能控制策略研究
發(fā)布時間：2018-10-08 11:07
【摘要】：隨著我國國民經濟的快速發(fā)展,人們對生活環(huán)境的舒適性要求越來越高。集中供熱事業(yè)是我國重要的基礎性事業(yè),是保證我國北方地區(qū)人們冬季生活環(huán)境舒適性的重要手段。當前我國能源于環(huán)保形勢嚴峻,粗放式集中供熱方式不滿足綠色發(fā)展要求。依靠先進的技術手段與控制策略來保證集中供熱系統(tǒng)的高效節(jié)能運行是建筑集中供熱系統(tǒng)發(fā)展的趨勢。隨著計量與監(jiān)控技術、網絡控制技術、信息處理技術的發(fā)展,依托這些先進技術,研究建筑集中供熱系統(tǒng)節(jié)能控制策略,提升集中供熱系統(tǒng)運行能效,已經成為相關領域研究和關注的重要內容。本文基于陜西地區(qū)某熱力公司的供熱技術平臺的數(shù)據(jù),考慮負荷動態(tài)調節(jié)要求,以末端負荷預測為目的,研究具有動態(tài)調節(jié)特征的負荷預測方法,在末端動態(tài)負荷預測的基礎上,提出了集中供熱系統(tǒng)末端設備節(jié)能控制策略和換熱站節(jié)能控制策略,以期提升集中供熱系統(tǒng)運行節(jié)能效率。本文首先分析了現(xiàn)行供熱系統(tǒng)變流量控制策略與熱負荷預測方法中存在的問題。現(xiàn)行供熱系統(tǒng)的變流量控制策略主要有“溫差”和“壓差”控制策略,這兩種方法都是基于系統(tǒng)熱負荷的集中效應進行的控制,不能完全滿足供熱系統(tǒng)末端用戶動態(tài)調節(jié)的要求和保證所用末端用戶的熱舒適性。隨著供熱系統(tǒng)計量與監(jiān)控技術發(fā)展,我們可以比較方便地獲得所用末端用戶的環(huán)境與運行參數(shù),利用這些參數(shù),研究合適的動態(tài)負荷方法,改善現(xiàn)行供熱系統(tǒng)變流量控制策略的不足,是本文研究的核心內容。通過對影響熱負荷變化的因素進行分析,發(fā)現(xiàn)供熱系統(tǒng)負荷變化受多種因素影響,并具有很強的非線性和不確定性。通過供熱系統(tǒng)常用的負荷預測算法進行簡單的分析,發(fā)現(xiàn)目前熱負荷預測算法具有算法復雜、關注集中效應、不具備動態(tài)調節(jié)特征等局限。為了目前熱負荷預測算法中存在的問題。本文從負荷數(shù)據(jù)預測與曲線擬合的相似性出發(fā),引入了移動多項式最小二乘預測模型;考慮供熱系統(tǒng)負荷變化較為平緩而且趨勢性較為明顯的特點,采用改進加權移動平均算法對末端負荷進行了預測。本文用相同的、來自實際工程的熱負荷數(shù)據(jù),分別采用移動多項式最小二乘預測模型、改進加權移動平均算法預測模型與目前負荷預測研究領域的熱點算法BP神經網絡算法進行了預測計算,并對預測結果進行了對比分析。預測結果表明:移動多項式最小二乘算法和改進加權移動平均算法較BP神經網絡算法的平均預測誤差小,算法更為簡單,所需數(shù)據(jù)也較少,更能適用于集中供熱系統(tǒng)末端熱負荷預測的工程應用。最后,根據(jù)對末端用戶熱負荷動態(tài)預測的結果,結合供熱系統(tǒng)的網絡控制與管理平臺的功能,本文提出了基于末端用戶熱負荷動態(tài)預測的節(jié)能控制策略,可分別實現(xiàn)用戶末端設備調節(jié)和換熱站負荷調節(jié)的節(jié)能運行優(yōu)化控制,力圖實現(xiàn)整個熱網的按需供熱,有助于提高整個供熱系統(tǒng)運行效率,實現(xiàn)節(jié)能減排的目標。
[Abstract]:With the rapid development of our national economy, people's comfort in living environment is getting higher and higher. Central heating is an important basic cause of our country, and it is an important means to ensure the comfort of people living in winter in northern China. The current energy of our country is severe in environmental protection, and the mode of centralized heating does not meet the requirement of green development. By means of advanced technical means and control strategy, the efficient energy-saving operation of central heating system is the trend of the development of central heating system. With the development of measurement and monitoring technology, network control technology and information processing technology, based on these advanced technologies, this paper studies the energy-saving control strategy of central heating system and improves the energy efficiency of central heating system. It has become an important content of research and attention in relevant fields. Based on the data of the heat supply technology platform of a thermal company in Shaanxi area, the load forecasting method with dynamic adjustment characteristics is studied in consideration of the load dynamic adjustment requirement, and the load forecasting method with the dynamic adjustment characteristic is researched, and on the basis of the end dynamic load forecasting, The energy-saving control strategy and energy-saving control strategy of heat exchange station in the end of central heating system are put forward in order to improve the efficiency of energy-saving in central heating system. In this paper, the existing problems in current heating system variable flow control strategy and thermal load forecasting method are analyzed. The variable flow control strategy of the current heating system mainly has the temperature difference and the pressure difference control strategy, both methods are based on the centralized effect of the thermal load of the system, can not completely meet the requirement of the dynamic regulation of the end user of the heating system and guarantee the thermal comfort of the end user. With the development of metering and monitoring technology of heat supply system, we can obtain the environment and operating parameters of the end user conveniently, utilize these parameters, study the suitable dynamic load method, improve the deficiency of the current variable flow control strategy of the current heating system, It is the core content of this paper. By analyzing the factors affecting the change of heat load, it is found that the load variation of heating system is influenced by many factors, and has strong nonlinearity and uncertainty. Through simple analysis of load forecasting algorithm commonly used in heat supply system, it is found that the current thermal load forecasting algorithm has the limitation of complex algorithm, focus effect, dynamic adjustment feature and so on. in order to solve the problems existing in the current thermal load prediction algorithm. In this paper, based on the similarity of load data prediction and curve fitting, the paper introduces the model of the least two-multiplication prediction of mobile polynomial, considers that the load variation of heat supply system is gentle and the trend is obvious, and adopts the improved weighted moving average algorithm to forecast the end load. In this paper, using the same data of thermal load from the actual engineering, we use the least squares prediction model of the mobile polynomial, improve the prediction model of the weighted moving average algorithm and the BP neural network algorithm of the hot spot algorithm in the current load forecasting research field, and make the prediction calculation. The prediction results are compared and analyzed. The results show that the least squares algorithm and the improved weighted moving average algorithm of the mobile polynomial are smaller than the average prediction error of the BP neural network algorithm, the algorithm is simpler, the required data is less, and more applicable to the engineering application of the heat load forecasting at the end of the central heating system. Finally, according to the result of the dynamic forecast of the end user's thermal load, combined with the function of the network control and management platform of the heat supply system, this paper proposes the energy-saving control strategy based on the dynamic forecast of the end user's thermal load. the energy-saving operation optimization control of the user end equipment regulation and the heat exchange station load regulation can be respectively realized, the demand of the whole heat supply network is realized, the operation efficiency of the whole heating system is improved, and the aim of energy saving and emission reduction is realized.
【學位授予單位】：廣州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU995
[Abstract]:With the rapid development of our national economy, people's comfort in living environment is getting higher and higher. Central heating is an important basic cause of our country, and it is an important means to ensure the comfort of people living in winter in northern China. The current energy of our country is severe in environmental protection, and the mode of centralized heating does not meet the requirement of green development. By means of advanced technical means and control strategy, the efficient energy-saving operation of central heating system is the trend of the development of central heating system. With the development of measurement and monitoring technology, network control technology and information processing technology, based on these advanced technologies, this paper studies the energy-saving control strategy of central heating system and improves the energy efficiency of central heating system. It has become an important content of research and attention in relevant fields. Based on the data of the heat supply technology platform of a thermal company in Shaanxi area, the load forecasting method with dynamic adjustment characteristics is studied in consideration of the load dynamic adjustment requirement, and the load forecasting method with the dynamic adjustment characteristic is researched, and on the basis of the end dynamic load forecasting, The energy-saving control strategy and energy-saving control strategy of heat exchange station in the end of central heating system are put forward in order to improve the efficiency of energy-saving in central heating system. In this paper, the existing problems in current heating system variable flow control strategy and thermal load forecasting method are analyzed. The variable flow control strategy of the current heating system mainly has the temperature difference and the pressure difference control strategy, both methods are based on the centralized effect of the thermal load of the system, can not completely meet the requirement of the dynamic regulation of the end user of the heating system and guarantee the thermal comfort of the end user. With the development of metering and monitoring technology of heat supply system, we can obtain the environment and operating parameters of the end user conveniently, utilize these parameters, study the suitable dynamic load method, improve the deficiency of the current variable flow control strategy of the current heating system, It is the core content of this paper. By analyzing the factors affecting the change of heat load, it is found that the load variation of heating system is influenced by many factors, and has strong nonlinearity and uncertainty. Through simple analysis of load forecasting algorithm commonly used in heat supply system, it is found that the current thermal load forecasting algorithm has the limitation of complex algorithm, focus effect, dynamic adjustment feature and so on. in order to solve the problems existing in the current thermal load prediction algorithm. In this paper, based on the similarity of load data prediction and curve fitting, the paper introduces the model of the least two-multiplication prediction of mobile polynomial, considers that the load variation of heat supply system is gentle and the trend is obvious, and adopts the improved weighted moving average algorithm to forecast the end load. In this paper, using the same data of thermal load from the actual engineering, we use the least squares prediction model of the mobile polynomial, improve the prediction model of the weighted moving average algorithm and the BP neural network algorithm of the hot spot algorithm in the current load forecasting research field, and make the prediction calculation. The prediction results are compared and analyzed. The results show that the least squares algorithm and the improved weighted moving average algorithm of the mobile polynomial are smaller than the average prediction error of the BP neural network algorithm, the algorithm is simpler, the required data is less, and more applicable to the engineering application of the heat load forecasting at the end of the central heating system. Finally, according to the result of the dynamic forecast of the end user's thermal load, combined with the function of the network control and management platform of the heat supply system, this paper proposes the energy-saving control strategy based on the dynamic forecast of the end user's thermal load. the energy-saving operation optimization control of the user end equipment regulation and the heat exchange station load regulation can be respectively realized, the demand of the whole heat supply network is realized, the operation efficiency of the whole heating system is improved, and the aim of energy saving and emission reduction is realized.
【學位授予單位】：廣州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU995
【參考文獻】
相關期刊論文 前10條
1 代曉東;王瀟瀟;畢曉光;楊景斌;印樹明;梁月;;2015年世界能源供需解讀——基于《BP世界能源統(tǒng)計年鑒》[J];天然氣與石油;2017年01期
2 劉雅琪;謝波;;基于改進加權移動平均法的鐵礦石到岸價格預測[J];上海海事大學學報;2015年02期
3 陳宗法;;從能源革命中尋求企業(yè)商機[J];中國電力企業(yè)管理;2015年05期
4 李浩然;方修睦;劉成;周志剛;;變流量運行供熱系統(tǒng)流量調節(jié)下限分析[J];煤氣與熱力;2015年02期
5 余宇峰;朱躍龍;萬定生;關興中;;基于滑動窗口預測的水文時間序列異常檢測[J];計算機應用;2014年08期
6 王魁吉;喬晨曄;梁德才;;供熱系統(tǒng)運行調節(jié)技術綜述[J];區(qū)域供熱;2013年05期
7 石兆玉;楊同球;;全網分布式輸配供熱系統(tǒng)的優(yōu)越性[J];區(qū)域供熱;2013年04期
8 石兆玉;;實施計量技術后供熱系統(tǒng)的控制決策[J];區(qū)域供熱;2012年05期
9 張毅;宋偉健;梅潔才;;滑動時間窗算法的Matlab實現(xiàn)[J];電腦編程技巧與維護;2012年10期
10 溫孝斌;;國內供熱節(jié)能中存在問題與解決途徑[J];科技傳播;2012年09期
相關會議論文 前1條
1 袁閃閃;朱能;田U，
本文編號：2256523
本文鏈接：http://www.lk138.cn/jianzhugongchenglunwen/2256523.html
最近更新
教材專著
