本文關(guān)鍵詞：基于監(jiān)測數(shù)據(jù)的建筑耗能設(shè)備運(yùn)行性能預(yù)測與分析研究　出處：《天津大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 建筑耗能設(shè)備 數(shù)據(jù)挖掘算法 SVM ANN 運(yùn)行性能 預(yù)測 三維溫度場

【摘要】：當(dāng)今世界能源形勢日益嚴(yán)峻,引起了世界各國的高度重視。我國能源消耗在近些年也呈現(xiàn)出急劇上升的態(tài)勢,建筑能耗是主要增長點(diǎn),其耗電量占全國電耗的22%左右,其中空調(diào)和供暖電耗約占全部建筑能耗的50%-70%,這部分能耗主要是在設(shè)備運(yùn)行過程中產(chǎn)生的。耗能設(shè)備按周期規(guī)律運(yùn)行可分為運(yùn)行階段和空閑階段,其中運(yùn)行階段又進(jìn)一步分為啟動階段、穩(wěn)定運(yùn)行階段和關(guān)閉階段。本文通過研究分析實(shí)測能耗數(shù)據(jù),確定以空閑階段為研究對象,建立了設(shè)備運(yùn)行空閑時間預(yù)測模型,旨在分析設(shè)備運(yùn)行規(guī)律,了解設(shè)備運(yùn)行性能,從而有效減少建筑物生命周期內(nèi)各設(shè)備的能耗,為設(shè)備節(jié)能和故障診斷奠定理論基礎(chǔ)。主要研究方法及研究成果有:(1)綜合分析耗能設(shè)備用電實(shí)測數(shù)據(jù)與周圍環(huán)境的氣象數(shù)據(jù),通過程序?qū)崿F(xiàn)了兩者的自動耦合,采集了一定時期內(nèi)不同時間段的建筑物能耗設(shè)備每分鐘的用電量及相應(yīng)的氣象數(shù)據(jù),進(jìn)而對相應(yīng)時期內(nèi)建筑物高能耗設(shè)備的運(yùn)行周期進(jìn)行了統(tǒng)計(jì),獲得了建筑物耗能設(shè)備運(yùn)行周期統(tǒng)計(jì)數(shù)據(jù),為設(shè)備運(yùn)行性能預(yù)測提供了數(shù)據(jù)基礎(chǔ)。(2)在獲得上述數(shù)據(jù)的基礎(chǔ)上,提出以空閑階段作為研究對象,對比分析支持向量機(jī)算法(SVM)、人工神經(jīng)網(wǎng)絡(luò)算法(ANN)、樸素貝葉斯算法(NB)和最鄰近算法(KNN)四種數(shù)據(jù)挖掘算法,計(jì)算結(jié)果顯示前兩種算法性能更優(yōu)。因此基于SVM算法和ANN算法建立預(yù)測模型并計(jì)算預(yù)測誤差,實(shí)現(xiàn)了建筑耗能設(shè)備運(yùn)行性能的預(yù)測。在這個過程中,依據(jù)樣本數(shù)據(jù)特征將其劃分成訓(xùn)練集和預(yù)測集,并在挖掘前對兩個集合實(shí)現(xiàn)非線性歸一化處理,此外通過交叉驗(yàn)證法或?qū)嶒?yàn)法確定了預(yù)測模型的參數(shù)。(3)基于三維建模軟件建立了建筑物幾何模型并對其網(wǎng)格劃分。利用紅外溫度儀監(jiān)測建筑物室內(nèi)墻面溫度后將溫度值定位于墻面,利用空間反比加權(quán)插值算法理論,采用MATLAB數(shù)據(jù)處理技術(shù),計(jì)算了室內(nèi)各剖分塊體中心溫度值,獲得了建筑物室內(nèi)墻面及內(nèi)部空間各中心點(diǎn)的所有溫度值,最終建立了室內(nèi)三維溫度場模型;結(jié)合所建立的溫度場模型和舒適度理論分析室內(nèi)熱環(huán)境,宏觀評價了耗能設(shè)備的運(yùn)行性能。
[Abstract]:Nowadays, the energy situation in the world is becoming more and more serious, which has aroused great attention from all over the world. In recent years, the energy consumption of our country has also shown a sharp rise, and the building energy consumption is the main growth point. Its electricity consumption accounts for about 22% of the country's electricity consumption, of which air conditioning and heating power consumption accounts for about 50% -70% of the total building energy consumption. This part of energy consumption is mainly generated in the equipment operation process. According to the cycle of energy consumption equipment operation can be divided into operation phase and idle stage, in which the operation phase is further divided into start-up phase. By studying and analyzing the measured energy consumption data, this paper determines the idle stage as the research object, and establishes a prediction model of the idle time of the equipment operation, aiming at analyzing the operation rule of the equipment. Understand the performance of the equipment to effectively reduce the building life cycle of each equipment energy consumption. It lays a theoretical foundation for energy saving and fault diagnosis of equipment. The main research methods and results are: 1) Comprehensive analysis of the measured data of energy consumption equipment and the meteorological data of the surrounding environment. Through the program to realize the automatic coupling between the two, the energy consumption per minute and the corresponding meteorological data of the building energy consumption equipment in a certain period of time are collected. Furthermore, the operation cycle of the building energy consuming equipment in the corresponding period is counted, and the statistical data of the operation cycle of the building energy consumption equipment are obtained. This paper provides a data base for performance prediction of equipment. (2) on the basis of obtaining the above data, this paper presents a comparative analysis of support vector machine (SVM) algorithm in the idle stage as the research object. Ann algorithm, naive Bayesian algorithm (NB) and nearest neighbor algorithm (KNN) are four kinds of data mining algorithms. The results show that the performance of the first two algorithms is better. Therefore, based on SVM algorithm and ANN algorithm, the prediction model is established and the prediction error is calculated to achieve the performance prediction of building energy consumption equipment. It is divided into training set and prediction set according to the feature of sample data, and the nonlinear normalization of the two sets is realized before mining. In addition, the parameters of the prediction model are determined by the cross validation method or the experimental method. Based on the 3D modeling software, the geometric model of the building is established and its mesh is divided. The temperature of the building indoor wall is monitored by the infrared temperature meter, and the temperature value is located on the wall. Based on the theory of inverse proportional weighted interpolation algorithm and MATLAB data processing technique, the temperature values of the center of each subdivision block in the room are calculated. All the temperature values of the interior wall and each center point of the interior space are obtained, and finally the indoor three-dimensional temperature field model is established. Combined with the established temperature field model and comfort theory, the indoor thermal environment was analyzed, and the operating performance of the energy dissipation equipment was evaluated macroscopically.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU111.195

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