【摘要】：隨著通信基站用電量的不斷增加,基站耗能的管控變得越來(lái)越重要,基于對(duì)公共建筑建立能耗標(biāo)桿的方法的研究,進(jìn)行數(shù)據(jù)挖掘的三種方法引用,從而建立通信基站能耗標(biāo)桿。首先利用多元線性回歸建立基站能耗標(biāo)桿,確立影響基站耗電量的重要影響因素,通過(guò)標(biāo)桿的分析結(jié)果給出節(jié)能和能耗管理建議;然后引用聚類算法將大量基站能耗數(shù)據(jù)分成8類,代表8種典型的基站能耗模式,通過(guò)分析能耗標(biāo)桿得到通信基站的耗能特點(diǎn)及現(xiàn)行基站能耗管理的不足與建議;接著引用人工神經(jīng)網(wǎng)絡(luò)方法來(lái)預(yù)測(cè)基站全年能耗,預(yù)測(cè)精度達(dá)到最大相對(duì)誤差為7.55%的水平,同時(shí),根據(jù)人工神經(jīng)網(wǎng)絡(luò)的重要性分析得到影響基站能耗的最重要因素分別為:主設(shè)備功率,空調(diào)能效比和氣溫,從而給出節(jié)能應(yīng)從這三方面抓起的管理建議。最后,對(duì)比多元線性回歸、聚類分析和人工神經(jīng)網(wǎng)絡(luò)三種方法的優(yōu)勢(shì)和劣勢(shì),給出應(yīng)用場(chǎng)景建議,多元線性回歸為最經(jīng)濟(jì)簡(jiǎn)便的數(shù)據(jù)挖掘方法,聚類分析為最能獲取深度知識(shí)的數(shù)據(jù)挖掘方法,人工神經(jīng)網(wǎng)絡(luò)為最適合應(yīng)用于預(yù)測(cè)的數(shù)據(jù)挖掘方法,并綜合三種方法的分析結(jié)果,給出通信基站的能耗管理建議。
[Abstract]:With the increasing of the power consumption of the communication base station, the control of the base station energy consumption becomes more and more important. Based on the research on the method of establishing the energy consumption benchmark in the public building, three methods of data mining are used to establish the energy consumption benchmark of the communication base station. Firstly, the energy consumption benchmark of base station is established by multivariate linear regression, and the important influencing factors of base station power consumption are established. The energy saving and energy consumption management suggestions are given through the analysis results of benchmark. Then a large number of base station energy consumption data are divided into 8 categories, representing 8 typical base station energy consumption modes. The characteristics of energy consumption of communication base station and the shortcomings and suggestions of current base station energy consumption management are obtained by analyzing the energy consumption benchmark. Then the artificial neural network method is used to predict the annual energy consumption of the base station, and the prediction accuracy reaches the maximum relative error level of 7.55%, at the same time, According to the analysis of the importance of artificial neural network, the most important factors affecting the energy consumption of base station are the power of main equipment, the energy efficiency ratio of air conditioning and the air temperature, respectively, and the management suggestions for energy saving should be made from these three aspects. Finally, compared with the advantages and disadvantages of the three methods of multivariate linear regression, clustering analysis and artificial neural network, the paper gives the suggestion of the application scenario that multivariate linear regression is the most economical and convenient method for data mining. Clustering analysis is the data mining method that can obtain the most deep knowledge, and artificial neural network is the most suitable data mining method for prediction. The analysis results of the three methods are synthesized, and some suggestions for energy consumption management of communication base stations are given.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 薛善斌;馬波;姜峰;;基于能耗PUE標(biāo)桿進(jìn)行通信基站能耗管控的思路和辦法[J];通信電源技術(shù);2014年S1期

2 張輝輝;胡記文;李婷君;;移動(dòng)通信發(fā)展史綜述[J];電子制作;2013年24期

3 李江坤;張春海;;通信行業(yè)能耗現(xiàn)狀分析與節(jié)能戰(zhàn)略思考[J];信息通信;2013年06期

4 高波;;基站主設(shè)備節(jié)能技術(shù)的典型應(yīng)用[J];內(nèi)蒙古科技與經(jīng)濟(jì);2012年18期

5 姜寧;;基站電費(fèi)“標(biāo)桿站”的研究[J];江蘇通信;2012年02期

6 張英杰;章兢;劉文強(qiáng);姚s，

本文編號(hào)：2128632