發(fā)布時(shí)間：2018-05-14 07:44

  本文選題：集中供熱系統(tǒng) + 熱負(fù)荷預(yù)測�。� 參考：《長春工業(yè)大學(xué)》2017年碩士論文

【摘要】：我國北方城鎮(zhèn)冬季供暖所需的供熱能耗在社會能源消耗中占的比重很大,隨著國家對節(jié)約能源的日益重視,大部分地區(qū)都采用了集中供熱的供暖方式。但是由于集中供熱系統(tǒng)覆蓋區(qū)域廣闊,控制和調(diào)節(jié)十分困難,因此在供熱系統(tǒng)運(yùn)行調(diào)節(jié)過程中對熱用戶的實(shí)際供熱量進(jìn)行預(yù)測顯得十分重要。為了能夠更加準(zhǔn)確的對集中供熱系統(tǒng)進(jìn)行熱負(fù)荷預(yù)測,本文對熱負(fù)荷的影響因素進(jìn)行了相關(guān)性分析,在確定預(yù)測模型的輸入變量和評價(jià)標(biāo)準(zhǔn)的基礎(chǔ)上,將神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)用于熱負(fù)荷預(yù)測,建立了各自不同的優(yōu)化改進(jìn)模型,并在熱負(fù)荷預(yù)測方面的表現(xiàn)做具體的研究。對于神經(jīng)網(wǎng)絡(luò)算法,本文建立了BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型,然后用小波分析理論對模型進(jìn)行了改進(jìn)并建立了小波神經(jīng)網(wǎng)絡(luò)預(yù)測模型;對于支持向量機(jī)算法,本文建立了支持向量機(jī)回歸預(yù)測模型,采用粒子群算法對支持向量機(jī)預(yù)測模型進(jìn)行參數(shù)尋優(yōu),并由此建立基于粒子群支持向量機(jī)預(yù)測模型。為了對支持向量機(jī)學(xué)習(xí)能力進(jìn)一步提高,故本文采用了動態(tài)多種群粒子群優(yōu)化支持向量機(jī)算法,并由此建立了基于動態(tài)多種群粒子群支持向量機(jī)熱負(fù)荷預(yù)測模型。通過對各種算法的集中供熱熱負(fù)荷預(yù)測模型的分析和計(jì)算結(jié)果表明:支持向量機(jī)算法比神經(jīng)網(wǎng)絡(luò)在處理與供熱負(fù)荷有關(guān)的較多影響因素的高維數(shù)學(xué)問題方面更為先進(jìn);動態(tài)多種群粒子群算法在參數(shù)尋優(yōu)中的搜索能力明顯要優(yōu)與粒子群算法;優(yōu)化后的預(yù)測模型的預(yù)測精度要高于原始預(yù)測模型;使用支持向量機(jī)及其優(yōu)化算法建立的集中供熱系統(tǒng)熱負(fù)荷預(yù)測模型的預(yù)測效果整體優(yōu)于使用神經(jīng)網(wǎng)絡(luò)及其優(yōu)化算法建立的預(yù)測模型。在基于供熱的實(shí)測數(shù)據(jù)的基礎(chǔ)上,通過分析、對比各個(gè)模型,并綜合評價(jià)因素,本文采用的基于動態(tài)多種群粒子群支持向量機(jī)熱負(fù)荷預(yù)測模型穩(wěn)定性好,預(yù)測精度高,能夠精確有效的為供暖企業(yè)科學(xué)生產(chǎn)提供有效的參考,為熱源分配、調(diào)度提供必要依據(jù)。
[Abstract]:The energy consumption of heating in northern cities and towns in winter accounts for a large proportion of the social energy consumption. With the increasing attention paid by the state to energy conservation, central heating is used in most areas. However, because the central heating system covers a wide area, it is very difficult to control and regulate, so it is very important to predict the actual heat supply of heat users in the operation and regulation process of the heating system. In order to forecast the heat load of the central heating system more accurately, this paper analyzes the influence factors of the heat load, on the basis of determining the input variables and evaluation criteria of the prediction model. Neural network and support vector machine are applied to heat load forecasting. Different optimization and improvement models are established, and the performance of heat load forecasting is studied in detail. For the neural network algorithm, the BP neural network prediction model is established, and then the wavelet analysis theory is used to improve the model and establish the wavelet neural network prediction model. In this paper, the regression prediction model of support vector machine is established, and the parameter optimization of support vector machine prediction model is carried out by using particle swarm optimization algorithm, and the prediction model based on particle swarm optimization support vector machine is established. In order to further improve the learning ability of support vector machine (SVM), the support vector machine (SVM) algorithm based on dynamic multi-swarm particle swarm optimization (DMLPSO) is adopted in this paper, and a heat load forecasting model based on DVM (support Vector Machine) is established. The analysis and calculation results of various algorithms show that the SVM algorithm is more advanced than the neural network in dealing with the high-dimensional mathematical problems related to the heat supply load. The search ability of dynamic multi-swarm optimization algorithm in parameter optimization is obviously superior to that of particle swarm optimization, and the prediction accuracy of the optimized prediction model is higher than that of the original prediction model. The forecasting effect of the heat load forecasting model of central heating system based on support vector machine and its optimization algorithm is better than that established by neural network and its optimization algorithm. On the basis of the measured data based on heating, through analyzing, comparing each model, and synthetically evaluating factors, the heat load forecasting model based on dynamic multi-swarm optimization support vector machine has good stability and high prediction precision. It can provide an effective reference for the scientific production of heating enterprises and provide the necessary basis for the distribution and dispatch of heat sources.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TU995;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王偉;;基于小波神經(jīng)網(wǎng)絡(luò)的電力系統(tǒng)負(fù)荷預(yù)測[J];科技創(chuàng)業(yè)月刊;2017年05期

2 蔡麒;;氣象因素與供熱負(fù)荷的關(guān)系研究[J];區(qū)域供熱;2016年04期

3 吳怡之;席戀;;基于支持向量機(jī)分類的腦中風(fēng)微波檢測[J];微型機(jī)與應(yīng)用;2016年13期

4 張震;徐子怡;張龍;袁淑芳;;基于小波神經(jīng)網(wǎng)絡(luò)的熱負(fù)荷預(yù)測方法[J];自動化技術(shù)與應(yīng)用;2016年05期

5 馬海;楊錦舟;肖紅兵;劉慶龍;王延江;;基于變異函數(shù)及支持向量機(jī)測井曲線插值方法[J];測井技術(shù);2012年06期

6 宋曉華;祖丕娥;伊靜;劉達(dá);;基于改進(jìn)GM(1,1)和SVM的長期電量優(yōu)化組合預(yù)測模型[J];中南大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年05期

7 劉葉玲;杜力博;;基于RS-SVM在電力短期負(fù)荷預(yù)測中的應(yīng)用[J];科技信息;2009年01期

8 張德山;王保民;陳正洪;李迅;王志斌;;北京市城市集中供熱節(jié)能氣象預(yù)報(bào)系統(tǒng)的應(yīng)用[J];煤氣與熱力;2008年11期

9 黎展求;朱棟華;;小波分析和SVR在供熱負(fù)荷預(yù)測中的應(yīng)用[J];科技咨詢導(dǎo)報(bào);2007年02期

10 黃文,管昌生;城市集中供熱研究現(xiàn)狀及發(fā)展趨勢[J];國外建材科技;2004年05期

相關(guān)會議論文 前1條

1 趙紅霞;孫春華;耿欣欣;王超;齊承英;;太陽輻射對供熱負(fù)荷的影響分析[A];2016供熱工程建設(shè)與高效運(yùn)行研討會會議論文專題報(bào)告[C];2016年

相關(guān)博士學(xué)位論文 前2條

1 介鵬飛;集中供暖系統(tǒng)熱負(fù)荷預(yù)測及運(yùn)行優(yōu)化[D];天津大學(xué);2013年

2 李丹;粒子群優(yōu)化算法及其應(yīng)用研究[D];東北大學(xué);2007年

相關(guān)碩士學(xué)位論文 前3條

1 李勝濤;集中供熱系統(tǒng)的熱負(fù)荷預(yù)測方法研究[D];長安大學(xué);2014年

2 龔文龍;基于最小二乘支持向量機(jī)的短期負(fù)荷預(yù)測[D];湖南大學(xué);2014年

3 李佳;基于粒子群優(yōu)化支持向量機(jī)的異常入侵檢測研究[D];中南林業(yè)科技大學(xué);2009年

，

本文編號：1886995