發(fā)布時間：2018-06-02 14:03

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

【摘要】：隨著我國經(jīng)濟社會的快速發(fā)展，社會的能耗問題日益突出，公共機構作為社會能源消耗的重要群體，其能耗的分析與預測尤其重要。公共機構能耗影響因素眾多，除去便于統(tǒng)計的人數(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、財政撥款等其他非確定因素給能耗分析帶來一定難度。本文依據(jù)天津市公共機構能耗統(tǒng)計平臺歷史數(shù)據(jù)對天津市公共機構能耗進行了分析和預測，主要工作有以下幾個方面。 1）對天津市公共機構概況進行分析，從公共機構數(shù)量、組成等方面得出近年來的變化趨勢。分析了公共機構能耗種類及支出類型，，得出公共機構能耗的特點：非營利性、缺少控制動因和相對穩(wěn)定性。從影響公共機構能耗的因素包括：建筑面積、公用車輛數(shù)量、用能人數(shù)、公共機構類型等方面對天津市2005至2010年公共機構的能耗數(shù)據(jù)進行了詳細描述。公共機構總能耗趨于穩(wěn)定變化不大，人均能耗逐年降低。 2）確定影響公共機構能耗的影響因素。影響公共機構能耗的因素眾多，不僅包括建筑類型、暖通結構、照明系統(tǒng)等硬件設施還包括非確定性因素。本文根據(jù)獲得的能耗數(shù)據(jù)，運用灰色關聯(lián)理論對現(xiàn)有能耗指標進行灰色關聯(lián)分析，得出影響公共機構電耗的關鍵指標有：建筑面積、用能人數(shù)、編制人數(shù)和機構類型。 3)建立基于遺傳神經(jīng)網(wǎng)絡的公共機構能耗分析模型。公共機構能耗組成具有高度的非線性特點，而人工神經(jīng)網(wǎng)絡具有很好的非線性、自學習與自適應能力，并且適用于處理多變量系統(tǒng)和很好的容錯能力，選取BP神經(jīng)網(wǎng)絡進行能耗的預測。BP神經(jīng)網(wǎng)絡自身的缺陷，初始權值選擇的盲目性會導致網(wǎng)絡陷入局部最小，而基于遺傳學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BP神經(jīng)網(wǎng)絡進行優(yōu)化。通過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)絡的初始權值和閾值，并訓練了網(wǎng)絡結構，克服了BP神經(jīng)網(wǎng)絡的缺陷。 4）運用MATLAB語言完成能耗預測模型的仿真，選取100組天津市公共機構的能耗統(tǒng)計數(shù)據(jù)對遺傳神經(jīng)網(wǎng)絡進行訓練，驗證模型的有效性，并將模型與標準BP神經(jīng)網(wǎng)絡進行比較，得出該模型優(yōu)于標準BP神經(jīng)網(wǎng)絡，并運用模型對5家公共機構的能耗進行了預測。
[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.
【學位授予單位】：天津理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP183;F206

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本文編號：1969026