集中供熱系統(tǒng)動(dòng)態(tài)負(fù)荷預(yù)測(cè)與節(jié)能控制策略研究
發(fā)布時(shí)間：2018-10-08 11:07
【摘要】：隨著我國(guó)國(guó)民經(jīng)濟(jì)的快速發(fā)展,人們對(duì)生活環(huán)境的舒適性要求越來(lái)越高。集中供熱事業(yè)是我國(guó)重要的基礎(chǔ)性事業(yè),是保證我國(guó)北方地區(qū)人們冬季生活環(huán)境舒適性的重要手段。當(dāng)前我國(guó)能源于環(huán)保形勢(shì)嚴(yán)峻,粗放式集中供熱方式不滿足綠色發(fā)展要求。依靠先進(jìn)的技術(shù)手段與控制策略來(lái)保證集中供熱系統(tǒng)的高效節(jié)能運(yùn)行是建筑集中供熱系統(tǒng)發(fā)展的趨勢(shì)。隨著計(jì)量與監(jiān)控技術(shù)、網(wǎng)絡(luò)控制技術(shù)、信息處理技術(shù)的發(fā)展,依托這些先進(jìn)技術(shù),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(yùn)行能效,已經(jīng)成為相關(guān)領(lǐng)域研究和關(guān)注的重要內(nèi)容。本文基于陜西地區(qū)某熱力公司的供熱技術(shù)平臺(tái)的數(shù)據(jù),考慮負(fù)荷動(dòng)態(tài)調(diào)節(jié)要求,以末端負(fù)荷預(yù)測(cè)為目的,研究具有動(dòng)態(tài)調(diào)節(jié)特征的負(fù)荷預(yù)測(cè)方法,在末端動(dòng)態(tài)負(fù)荷預(yù)測(cè)的基礎(chǔ)上,提出了集中供熱系統(tǒng)末端設(shè)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(yùn)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(fù)荷預(yù)測(cè)方法中存在的問(wèn)題�，F(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(fù)荷的集中效應(yīng)進(jìn)行的控制,不能完全滿足供熱系統(tǒng)末端用戶動(dòng)態(tài)調(diào)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(jì)量與監(jiān)控技術(shù)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(yùn)行參數(shù),利用這些參數(shù),研究合適的動(dòng)態(tài)負(fù)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(nèi)容。通過(guò)對(duì)影響熱負(fù)荷變化的因素進(jìn)行分析,發(fā)現(xiàn)供熱系統(tǒng)負(fù)荷變化受多種因素影響,并具有很強(qiáng)的非線性和不確定性。通過(guò)供熱系統(tǒng)常用的負(fù)荷預(yù)測(cè)算法進(jìn)行簡(jiǎn)單的分析,發(fā)現(xiàn)目前熱負(fù)荷預(yù)測(cè)算法具有算法復(fù)雜、關(guān)注集中效應(yīng)、不具備動(dòng)態(tài)調(diào)節(jié)特征等局限。為了目前熱負(fù)荷預(yù)測(cè)算法中存在的問(wèn)題。本文從負(fù)荷數(shù)據(jù)預(yù)測(cè)與曲線擬合的相似性出發(fā),引入了移動(dòng)多項(xiàng)式最小二乘預(yù)測(cè)模型;考慮供熱系統(tǒng)負(fù)荷變化較為平緩而且趨勢(shì)性較為明顯的特點(diǎn),采用改進(jìn)加權(quán)移動(dòng)平均算法對(duì)末端負(fù)荷進(jìn)行了預(yù)測(cè)。本文用相同的、來(lái)自實(shí)際工程的熱負(fù)荷數(shù)據(jù),分別采用移動(dòng)多項(xiàng)式最小二乘預(yù)測(cè)模型、改進(jìn)加權(quán)移動(dòng)平均算法預(yù)測(cè)模型與目前負(fù)荷預(yù)測(cè)研究領(lǐng)域的熱點(diǎn)算法BP神經(jīng)網(wǎng)絡(luò)算法進(jìn)行了預(yù)測(cè)計(jì)算,并對(duì)預(yù)測(cè)結(jié)果進(jìn)行了對(duì)比分析。預(yù)測(cè)結(jié)果表明:移動(dòng)多項(xiàng)式最小二乘算法和改進(jìn)加權(quán)移動(dòng)平均算法較BP神經(jīng)網(wǎng)絡(luò)算法的平均預(yù)測(cè)誤差小,算法更為簡(jiǎn)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(fù)荷預(yù)測(cè)的工程應(yīng)用。最后,根據(jù)對(duì)末端用戶熱負(fù)荷動(dòng)態(tài)預(yù)測(cè)的結(jié)果,結(jié)合供熱系統(tǒng)的網(wǎng)絡(luò)控制與管理平臺(tái)的功能,本文提出了基于末端用戶熱負(fù)荷動(dòng)態(tài)預(yù)測(cè)的節(jié)能控制策略,可分別實(shí)現(xiàn)用戶末端設(shè)備調(diào)節(jié)和換熱站負(fù)荷調(diào)節(jié)的節(jié)能運(yùn)行優(yōu)化控制,力圖實(shí)現(xiàn)整個(gè)熱網(wǎng)的按需供熱,有助于提高整個(gè)供熱系統(tǒng)運(yùn)行效率,實(shí)現(xiàn)節(jié)能減排的目標(biāo)。
[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(xué)位授予單位】：廣州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：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(xué)位授予單位】：廣州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TU995
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