發(fā)布時(shí)間：2018-06-02 14:03

  本文選題：遺傳算法 + BP神經(jīng)網(wǎng)絡(luò)�。� 參考：《天津理工大學(xué)》2014年碩士論文

【摘要】：隨著我國(guó)經(jīng)濟(jì)社會(huì)的快速發(fā)展，社會(huì)的能耗問(wèn)題日益突出，公共機(jī)構(gòu)作為社會(huì)能源消耗的重要群體，其能耗的分析與預(yù)測(cè)尤其重要。公共機(jī)構(gòu)能耗影響因素眾多，除去便于統(tǒng)計(jì)的人數(shù)、建筑面積、車輛數(shù)目等因素還包括能耗管理制度、用能方式、財(cái)政撥款等其他非確定因素給能耗分析帶來(lái)一定難度。本文依據(jù)天津市公共機(jī)構(gòu)能耗統(tǒng)計(jì)平臺(tái)歷史數(shù)據(jù)對(duì)天津市公共機(jī)構(gòu)能耗進(jìn)行了分析和預(yù)測(cè)，主要工作有以下幾個(gè)方面。 1）對(duì)天津市公共機(jī)構(gòu)概況進(jìn)行分析，從公共機(jī)構(gòu)數(shù)量、組成等方面得出近年來(lái)的變化趨勢(shì)。分析了公共機(jī)構(gòu)能耗種類及支出類型，，得出公共機(jī)構(gòu)能耗的特點(diǎn)：非營(yíng)利性、缺少控制動(dòng)因和相對(duì)穩(wěn)定性。從影響公共機(jī)構(gòu)能耗的因素包括：建筑面積、公用車輛數(shù)量、用能人數(shù)、公共機(jī)構(gòu)類型等方面對(duì)天津市2005至2010年公共機(jī)構(gòu)的能耗數(shù)據(jù)進(jìn)行了詳細(xì)描述。公共機(jī)構(gòu)總能耗趨于穩(wěn)定變化不大，人均能耗逐年降低。 2）確定影響公共機(jī)構(gòu)能耗的影響因素。影響公共機(jī)構(gòu)能耗的因素眾多，不僅包括建筑類型、暖通結(jié)構(gòu)、照明系統(tǒng)等硬件設(shè)施還包括非確定性因素。本文根據(jù)獲得的能耗數(shù)據(jù)，運(yùn)用灰色關(guān)聯(lián)理論對(duì)現(xiàn)有能耗指標(biāo)進(jìn)行灰色關(guān)聯(lián)分析，得出影響公共機(jī)構(gòu)電耗的關(guān)鍵指標(biāo)有：建筑面積、用能人數(shù)、編制人數(shù)和機(jī)構(gòu)類型。 3)建立基于遺傳神經(jīng)網(wǎng)絡(luò)的公共機(jī)構(gòu)能耗分析模型。公共機(jī)構(gòu)能耗組成具有高度的非線性特點(diǎn)，而人工神經(jīng)網(wǎng)絡(luò)具有很好的非線性、自學(xué)習(xí)與自適應(yīng)能力，并且適用于處理多變量系統(tǒng)和很好的容錯(cuò)能力，選取BP神經(jīng)網(wǎng)絡(luò)進(jìn)行能耗的預(yù)測(cè)。BP神經(jīng)網(wǎng)絡(luò)自身的缺陷，初始權(quán)值選擇的盲目性會(huì)導(dǎo)致網(wǎng)絡(luò)陷入局部最小，而基于遺傳學(xué)與自然選擇的遺傳算法擁有全局尋優(yōu)的能力，選取遺傳算法對(duì)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行優(yōu)化。通過(guò)遺傳算法初始種群的生成，選擇、交叉和變異操作確定神經(jīng)網(wǎng)絡(luò)的初始權(quán)值和閾值，并訓(xùn)練了網(wǎng)絡(luò)結(jié)構(gòu)，克服了BP神經(jīng)網(wǎng)絡(luò)的缺陷。 4）運(yùn)用MATLAB語(yǔ)言完成能耗預(yù)測(cè)模型的仿真，選取100組天津市公共機(jī)構(gòu)的能耗統(tǒng)計(jì)數(shù)據(jù)對(duì)遺傳神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練，驗(yàn)證模型的有效性，并將模型與標(biāo)準(zhǔn)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行比較，得出該模型優(yōu)于標(biāo)準(zhǔn)BP神經(jīng)網(wǎng)絡(luò)，并運(yùn)用模型對(duì)5家公共機(jī)構(gòu)的能耗進(jìn)行了預(yù)測(cè)。
[Abstract]:With the rapid development of our country's economy and society, the problem of energy consumption is becoming more and more prominent. As an important group of social energy consumption, it is very important to analyze and predict the energy consumption of public institutions. There are many factors affecting energy consumption in public institutions. Besides the factors such as the number of people, the building area, the number of vehicles and so on, such factors as energy consumption management system, energy use mode, financial allocation and other uncertain factors bring some difficulties to the energy consumption analysis. Based on the historical data of Tianjin public institution energy consumption statistical platform, this paper analyzes and forecasts the energy consumption of public institutions in Tianjin. The main work is as follows. 1) the general situation of Tianjin public institutions is analyzed, and the change trend in recent years is obtained from the number and composition of public institutions. The types of energy consumption and the types of expenditure of public institutions are analyzed. The characteristics of energy consumption of public institutions are as follows: non-profit, lack of control motivation and relative stability. The energy consumption data of public institutions in Tianjin from 2005 to 2010 are described in detail from the following factors: the building area, the number of public vehicles, the number of energy users and the types of public institutions. The total energy consumption of public institutions tends to change steadily, and the per capita energy consumption decreases year by year. 2) determine the factors that affect the energy consumption of public institutions. There are many factors that affect the energy consumption of public institutions, including not only the types of buildings, HVAC structures, lighting systems and other hardware facilities, but also non-deterministic factors. According to the energy consumption data obtained, the grey correlation analysis of the existing energy consumption index is carried out by using the grey correlation theory, and the key indexes affecting the power consumption of public institutions are obtained: building area, number of energy users, number of persons compiled and type of mechanism. 3) the energy consumption analysis model of public institutions based on genetic neural network is established. The energy consumption composition of common mechanism is highly nonlinear, while the artificial neural network has good nonlinear, self-learning and adaptive ability, and is suitable for multivariable systems and fault-tolerant. Selecting BP neural network to predict energy consumption. The defects of BP neural network itself, the blindness of initial weight selection will lead to the local minimum of the network, and the genetic algorithm based on genetics and natural selection has the ability of global optimization. Genetic algorithm is selected to optimize BP neural network. The initial weights and thresholds of neural networks are determined by genetic algorithm (GA) initial population generation, selection, crossover and mutation operations, and the network structure is trained to overcome the defects of BP neural networks. 4) using MATLAB language to simulate the energy consumption prediction model, 100 groups of energy consumption statistics of Tianjin public institutions are selected to train the genetic neural network to verify the validity of the model, and the model is compared with the standard BP neural network. It is concluded that the model is superior to the standard BP neural network, and the energy consumption of five public institutions is predicted by using the model.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP183;F206

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 孫曉光,傅云義,胡賡祥,王國(guó)棟,劉相華;遺傳算法對(duì)熱軋帶鋼精軋機(jī)組負(fù)荷分配BP網(wǎng)絡(luò)參數(shù)的優(yōu)化[J];鋼鐵;1998年07期

2 張碩鵬;李銳;;辦公類建筑能耗影響因素與節(jié)能潛力[J];北京建筑工程學(xué)院學(xué)報(bào);2013年01期

3 陳海波;王凡;;基于能耗分項(xiàng)計(jì)量數(shù)據(jù)的大型公建節(jié)能診斷方法及典型案例[J];建筑科學(xué);2011年04期

4 張瑞滋;呂挨梁;程繼東;;鄂爾多斯市公共機(jī)構(gòu)能耗指標(biāo)相關(guān)性分析[J];內(nèi)蒙古科技與經(jīng)濟(jì);2011年03期

5 王一凡;趙歡;王培紅;;能耗管理數(shù)據(jù)庫(kù)系統(tǒng)的開(kāi)發(fā)與利用[J];上海節(jié)能;2008年09期

6 陳程;趙歡;王培紅;;公共機(jī)構(gòu)建筑能耗指標(biāo)的多因素影響評(píng)價(jià)[J];上海節(jié)能;2009年02期

7 劉丹丹;陳啟軍;森一之;木田幸夫;;基于數(shù)據(jù)的建筑能耗分析與建模[J];同濟(jì)大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年12期

8 李敏強(qiáng),徐博藝,寇紀(jì)淞;遺傳算法與神經(jīng)網(wǎng)絡(luò)的結(jié)合[J];系統(tǒng)工程理論與實(shí)踐;1999年02期

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