發(fā)布時(shí)間：2018-02-05 20:25

  本文關(guān)鍵詞： 路燈開(kāi)關(guān)控制 預(yù)測(cè) 小樣本 最小二乘支持向量機(jī)　出處：《華中科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：路燈照明在市民出行、社會(huì)秩序和交通安全中處于特別重要的位置。近年來(lái),城市路燈每年以10%至20%的速度增長(zhǎng)。2014年全國(guó)城市路燈消耗約2,485億kWh電能,按每天平均照明12小時(shí),每天若能減少照明1分鐘,則每年大約可節(jié)約3.45億kWh,相當(dāng)于減少排放二氧化碳約34.4萬(wàn)噸。現(xiàn)有的城市路燈開(kāi)關(guān)控制大多是根據(jù)模板表定時(shí)觸發(fā),在正常天氣時(shí)經(jīng)常早晨關(guān)燈晚,晚上開(kāi)燈早,造成能源的浪費(fèi);在惡劣天氣時(shí)不能自動(dòng)提早開(kāi)燈或延遲關(guān)燈,需要工作人員手動(dòng)下發(fā)開(kāi)關(guān)燈命令,從命令下發(fā)到路燈完全點(diǎn)亮大約需要10~15分鐘,而這時(shí)天空已經(jīng)變得很暗,嚴(yán)重影響市民的正常生活甚至可能造成交通事故。結(jié)合現(xiàn)有路燈開(kāi)關(guān)控制的不足,提出將預(yù)測(cè)應(yīng)用到路燈開(kāi)關(guān)控制中,提前得到未來(lái)的開(kāi)關(guān)燈時(shí)間,不僅為路燈控制提供一定的決策支持,也能節(jié)約能源。本文研究了路燈開(kāi)關(guān)時(shí)間的特點(diǎn)和其影響因素,重點(diǎn)分析了光照度的特性,提出了天氣的數(shù)值化映射。路燈系統(tǒng)環(huán)境復(fù)雜,樣本數(shù)據(jù)受到各種影響,根據(jù)實(shí)驗(yàn)數(shù)據(jù)的特性介紹了本文使用的數(shù)據(jù)預(yù)處理方法。比較了現(xiàn)有常用的預(yù)測(cè)算法,因本實(shí)驗(yàn)數(shù)據(jù)的小樣本特性,選擇用最小二乘支持向量機(jī)進(jìn)行建模。依照訓(xùn)練數(shù)據(jù)的特性給出了最小二乘支持向量機(jī)的模型結(jié)構(gòu)、核函數(shù)選擇、參數(shù)優(yōu)化等。通過(guò)仿真分析可以發(fā)現(xiàn),與BP神經(jīng)網(wǎng)絡(luò)模型對(duì)比,最小二乘支持向量機(jī)在小樣本預(yù)測(cè)中的性能更好。當(dāng)光照度變化趨勢(shì)越具體時(shí),預(yù)測(cè)效果會(huì)越好。僅僅根據(jù)日落時(shí)間、最低溫度、最高溫度、天氣狀況,預(yù)測(cè)的平均絕對(duì)誤差在2分15秒之內(nèi)。相比日落時(shí)就開(kāi)燈,根據(jù)本文預(yù)測(cè)的時(shí)間進(jìn)行開(kāi)燈,每盞燈每天大約能節(jié)省7分33秒的照明時(shí)間。
[Abstract]:In recent years, urban street lighting is increasing at the rate of 10% to 20% per year. In 2014, the consumption of urban street lights in China was about 2. 48.5 billion kWh power, with an average of 12 hours of lighting per day, can save about 345 million kWh per year if the lighting is reduced by one minute per day. Most of the existing urban street lamp switch controls are triggered according to the template table timing, in normal weather often turn off the lights late in the morning, early in the evening. Waste of energy; In bad weather can not automatically turn on the lights early or late turn off the lights require staff to manually send off the switch light command from the command down to the street lights to be fully turned on about 1015 minutes. By this time, the sky has become very dark, seriously affect the normal life of citizens and even cause traffic accidents. Combined with the deficiency of the existing street lamp switch control, it is proposed that the prediction be applied to the street light switch control. Getting the future switching time in advance can not only provide certain decision support for street lamp control, but also save energy. This paper studies the characteristics of street lamp switching time and its influencing factors, focusing on the characteristics of light intensity. The numerical mapping of weather is presented. The environment of street lamp system is complex and the sample data is affected by various kinds of data. According to the characteristics of experimental data, the data preprocessing method used in this paper is introduced, and the commonly used prediction algorithms are compared. Because of the small sample characteristics of the experimental data, we choose the least squares support vector machine to model. According to the characteristics of the training data, the model structure and kernel function selection of the least squares support vector machine are given. Through the simulation analysis, it can be found that compared with BP neural network model, the least square support vector machine has better performance in small sample prediction. The better the prediction. Only based on sunset time, minimum temperature, maximum temperature, weather conditions, the average absolute error predicted is within 2 minutes 15 seconds. Lights are turned on compared to sunset. According to the time predicted in this paper, each lamp can save about 7 minutes 33 seconds lighting time per day.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TU113.666

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