發(fā)布時間：2018-06-24 19:16

  本文選題：數(shù)據(jù)挖掘 + 主成分分析�。� 參考：《北京建筑大學(xué)》2017年碩士論文

【摘要】：近年來,世界各國均將建筑節(jié)能減排工作視為經(jīng)濟(jì)發(fā)展的一項(xiàng)核心內(nèi)容。中國的建筑能耗大約占社會總體能耗的33%,但是隨著建筑物數(shù)量的增加以及人類居住潔凈度舒適度的提高,建筑能耗仍將呈繼續(xù)上升的趨向,因此建筑節(jié)能問題也是中國節(jié)能減排的重中之重。目前,我國進(jìn)行了一系列有關(guān)建筑節(jié)能減排的工作,許多科研工作者也都在建筑能耗監(jiān)測、能耗節(jié)能分析與能源故障診斷等方面展開了許多工作。本文將采用數(shù)據(jù)挖掘技術(shù),從建筑能源管理平臺中提取建筑能耗變量數(shù)據(jù)進(jìn)行建模,并以綜合性辦公建筑為研究對象,運(yùn)用建筑能耗故障診斷模型,對該建筑使用過程中的能耗異常擾動進(jìn)行識別和診斷研究,及時通知運(yùn)行維護(hù)人員能耗異常事件和系統(tǒng)故障所在,最終達(dá)到消除故障的目的。建筑能耗監(jiān)控與診斷系統(tǒng)對于提升能源使用效率、保障系統(tǒng)的運(yùn)行過程、保障設(shè)備和人員的安全等,具有非常重要的現(xiàn)實(shí)意義。首先,主成分分析是多元統(tǒng)計(jì)分析里面使用最廣泛的方法,選定主成分分析法作為本文數(shù)據(jù)挖掘的方法,確定最優(yōu)主成分個數(shù)的選擇方法,確定主成分分析模型的建立規(guī)則,確定基于主成分分析的故障診斷的統(tǒng)計(jì)量和控制限的計(jì)算方法,基于MATLAB程序?qū)崿F(xiàn)主成分分析;其次,利用Skyspark軟件創(chuàng)建智慧建筑能源管理平臺,把建筑中所有用能設(shè)備集中于該平臺,并且該智慧建筑還建立了氣象站,可以采集室外溫度、室外風(fēng)速、室外濕度和室外PM_(2.5)濃度。在此基礎(chǔ)上,按照能源種類和用途對建筑系統(tǒng)能耗統(tǒng)計(jì)和監(jiān)測,選取耗冷量、耗水量、空調(diào)用電量、照明用電量、景觀用電量、動力用電量、生活用電量、生產(chǎn)用電量和商業(yè)用電量,進(jìn)而分析能耗使用情況,并實(shí)現(xiàn)能耗統(tǒng)計(jì)與節(jié)能分析的展示,完善智慧能源管理平臺;再次,選取連續(xù)完整的100天建筑能耗輸入變量相關(guān)數(shù)據(jù),運(yùn)用MATLAB軟件建立智慧建筑能耗系統(tǒng)故障診斷主成分模型。根據(jù)實(shí)際變量數(shù)據(jù)與主成分模型進(jìn)行分析和對比,當(dāng)累計(jì)方差貢獻(xiàn)率CPV(k)=87.046%,主成分個數(shù)k=7,置信度α=99%,UCL=21.0524,Q=2.4262時,建立智慧能耗系統(tǒng)故障診斷模型,此時的診斷模型與實(shí)際過程最為吻合;最后,通過能源管理平臺采集建筑系統(tǒng)全年365天的能耗數(shù)據(jù),對已建立的智慧建筑能耗系統(tǒng)故障診斷模型進(jìn)行應(yīng)用,并建立相應(yīng)的故障檢測與診斷規(guī)則,發(fā)現(xiàn)T2統(tǒng)計(jì)量監(jiān)控圖在系統(tǒng)運(yùn)行過程中超出其控制限UCL=21.0524的有11處,SPE統(tǒng)計(jì)量監(jiān)控圖在系統(tǒng)運(yùn)行過程中超出其控制限Q=2.4262的較多,系統(tǒng)自動報警,從而判斷故障產(chǎn)生的原因。利用上述研究成果,若建筑系統(tǒng)的運(yùn)行能耗發(fā)生故障時,可以將系統(tǒng)變化特征與主成分模型進(jìn)行故障匹配,結(jié)合匹配結(jié)果,最終可以達(dá)到故障檢測與診斷。該結(jié)果為今后的建筑能源管理系統(tǒng)的故障診斷奠定了良好的基礎(chǔ)。
[Abstract]:In recent years, countries all over the world regard building energy conservation and emission reduction as a core content of economic development. Building energy consumption in China accounts for about 33 percent of the total energy consumption in society. However, with the increase in the number of buildings and the improvement of human living cleanliness and comfort, building energy consumption will continue to rise. Therefore, the building energy-saving problem is also the top priority of energy-saving and emission reduction in China. At present, China has carried out a series of work on building energy conservation and emission reduction, and many researchers have also carried out a lot of work in building energy consumption monitoring, energy saving analysis and energy fault diagnosis. In this paper, data mining technology is used to extract building energy consumption variable data from building energy management platform for modeling. Taking comprehensive office building as research object, building energy consumption fault diagnosis model is used. The abnormal disturbance of energy consumption in the use of the building is identified and diagnosed, and the abnormal energy consumption event and the fault of the system are notified to the maintenance personnel in time, finally the purpose of eliminating the fault is achieved. The monitoring and diagnosis system of building energy consumption is of great practical significance for improving the efficiency of energy use, ensuring the operation process of the system, and ensuring the safety of equipment and personnel. First of all, principal component analysis (PCA) is the most widely used method in multivariate statistical analysis. Principal component analysis (PCA) is selected as the method of data mining in this paper. The calculation method of statistical quantity and control limit of fault diagnosis based on principal component analysis is determined, and the principal component analysis is realized based on MATLAB program. Secondly, the intelligent building energy management platform is created by using Skyspark software. All the energy-using equipments in the building are concentrated on the platform, and the intelligent building has also set up a weather station, which can collect outdoor temperature, outdoor wind speed, outdoor humidity and outdoor PM2.5 concentration. On this basis, according to the energy consumption statistics and monitoring of the building system according to the types and uses of energy, select cooling consumption, water consumption, air conditioning power consumption, lighting electricity consumption, landscape electricity consumption, power consumption, living electricity consumption, Production and commercial electricity consumption, and then analysis of energy consumption, and achieve energy statistics and energy conservation analysis display, improve the intelligent energy management platform; thirdly, select 100 days of building energy input variables related data, The principal component model for fault diagnosis of intelligent building energy consumption system is established by using MATLAB software. According to the actual variable data and principal component model, when the cumulative contribution rate of variance is 87.046, the number of principal components is 7, and the confidence degree is 21.0524Q = 2.4262, the fault diagnosis model of intelligent energy consumption system is established, and the diagnosis model is most consistent with the actual process. Finally, the energy consumption data of the building system are collected through the energy management platform, and the established fault diagnosis model of the intelligent building energy consumption system is applied, and the corresponding fault detection and diagnosis rules are established. It is found that there are 11 SPE statistic monitoring charts which exceed the control limit UCLG 21.0524 during the operation of the system. There are more SPE statistics monitoring charts than its control limit Q2.4262 during the system operation, and the system automatically alarm, thus judging the cause of the failure. Using the above research results, if the running energy consumption of the building system fails, the system change characteristics can be matched with the principal component model, and the fault detection and diagnosis can be achieved by combining the matching results. The result lays a good foundation for the fault diagnosis of building energy management system in the future.
【學(xué)位授予單位】：北京建筑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TU111.195;TP311.13

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