本文選題：電力負(fù)荷預(yù)測(cè) + BP神經(jīng)網(wǎng)絡(luò)��； 參考：《華北電力大學(xué)》2013年碩士論文

【摘要】：電力負(fù)荷預(yù)測(cè)是電力系統(tǒng)安全經(jīng)濟(jì)規(guī)劃、調(diào)度、設(shè)計(jì)研究的必要和前提。準(zhǔn)確的負(fù)荷預(yù)測(cè)，不僅能夠幫助電力部門有效合理的制定發(fā)配電計(jì)劃，還能夠幫助電力部門減少能源浪費(fèi)，從而降低發(fā)電成本，提高企業(yè)的經(jīng)濟(jì)效益和社會(huì)效益。中長(zhǎng)期電力負(fù)荷預(yù)測(cè)是指5年左右或10年以上的負(fù)荷預(yù)測(cè)工作，它是電網(wǎng)規(guī)劃的重要基礎(chǔ)，其預(yù)測(cè)精度的好壞直接影響到電網(wǎng)規(guī)劃工作的優(yōu)劣，因此，電力負(fù)荷預(yù)測(cè)精度的提高是目前眾多研究者努力探尋的目標(biāo)。 目前，用于電力負(fù)荷預(yù)測(cè)的方法有很多種，，且每種預(yù)測(cè)方法都有自己的適用范圍，很難應(yīng)用于全部的預(yù)測(cè)情況。因此，我們必須根據(jù)具體的負(fù)荷預(yù)測(cè)特點(diǎn)，找出對(duì)應(yīng)的預(yù)測(cè)方法。這就要求我們對(duì)負(fù)荷特性等方面進(jìn)行充分的認(rèn)識(shí)和分析，并且能夠總結(jié)出影響電力負(fù)荷預(yù)測(cè)的主要影響因素，最后再按照根據(jù)具體的負(fù)荷預(yù)測(cè)特性選擇合適的預(yù)測(cè)方法，對(duì)歷史數(shù)據(jù)進(jìn)行計(jì)算處理，最終獲得理想的預(yù)測(cè)精度。 本文系統(tǒng)的對(duì)目前用于電力負(fù)荷預(yù)測(cè)的方法進(jìn)行了總結(jié)分析，并概述了當(dāng)前電力負(fù)荷預(yù)測(cè)的特性、原理及步驟。在對(duì)電力負(fù)荷特性的規(guī)律及影響電力負(fù)荷預(yù)測(cè)的各種因素充分分析基礎(chǔ)上，本文提出了基于蟻群算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的電力負(fù)荷預(yù)測(cè)模型；在分別對(duì)蟻群算法和BP神經(jīng)網(wǎng)絡(luò)充分介紹認(rèn)識(shí)后，根據(jù)包頭地區(qū)的電力負(fù)荷預(yù)測(cè)的實(shí)際情況，建立了基于蟻群算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的電力負(fù)荷預(yù)測(cè)模型，并利用相關(guān)分析法確定出網(wǎng)絡(luò)的輸入向量和輸出向量。 最后，本文利用蟻群算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型對(duì)包頭地區(qū)的電力負(fù)荷進(jìn)行預(yù)測(cè)分析，并與優(yōu)化前BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)值進(jìn)行對(duì)比分析，結(jié)果表明：蟻群優(yōu)化后的BP神經(jīng)網(wǎng)絡(luò)的預(yù)測(cè)精度要高于優(yōu)化前BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)精度，說(shuō)明蟻群算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型更加適合于該地區(qū)的電力負(fù)荷預(yù)測(cè)工作。
[Abstract]:Power load forecasting is the necessity and premise of power system safety and economic planning, dispatching and design research. Accurate load forecasting can not only help the power sector to make the distribution plan effectively and reasonably, but also can help the power sector to reduce the energy waste, thus reduce the cost of power generation, and improve the economic and social benefits of the enterprise. Medium-and long-term power load forecasting refers to the work of load forecasting about 5 years or more. It is an important foundation of power network planning, and the quality of forecasting accuracy directly affects the quality of power network planning. At present, there are many methods used in power load forecasting, and each method has its own scope of application, so it is difficult to be applied to all forecasting situations. Therefore, we must find out the corresponding forecasting methods according to the specific characteristics of load forecasting. This requires us to have a full understanding and analysis of the load characteristics, and to be able to sum up the main factors that affect the power load forecasting, and finally select the appropriate forecasting method according to the specific load forecasting characteristics. In this paper, the methods used in power load forecasting are summarized and analyzed systematically, and the characteristics, principles and steps of current power load forecasting are summarized. On the basis of analyzing the law of power load characteristic and various factors influencing power load forecasting, this paper puts forward a power load forecasting model based on ant colony algorithm optimization BP neural network. After introducing the ant colony algorithm and BP neural network respectively, according to the actual situation of power load forecasting in Baotou area, a power load forecasting model based on ant colony algorithm optimization BP neural network is established. The input vector and output vector of the network are determined by correlation analysis. Finally, the paper uses ant colony algorithm to optimize BP neural network forecasting model to forecast the power load in Baotou area. The results show that the prediction accuracy of BP neural network after ant colony optimization is higher than that of BP neural network model before optimization. It shows that the BP neural network forecasting model optimized by ant colony algorithm is more suitable for power load forecasting in this area.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F426.61;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蔣燕;王少楊;封蕓;;基于遞歸等權(quán)組合模型的中長(zhǎng)期電力負(fù)荷預(yù)測(cè)[J];電力系統(tǒng)及其自動(dòng)化學(xué)報(bào);2012年01期

2 王黎明;王艷松;;基于RBF神經(jīng)網(wǎng)絡(luò)的短期負(fù)荷預(yù)測(cè)[J];電氣技術(shù);2007年04期

3 李鵬飛;加瑪力汗·庫(kù)馬什;常喜強(qiáng);;幾種電力負(fù)荷預(yù)測(cè)方法的比較[J];電氣技術(shù);2011年06期

4 劉國(guó)志;何鵬清;;基于改進(jìn)微粒群優(yōu)化的電力負(fù)荷生長(zhǎng)曲線預(yù)測(cè)模型[J];遼寧石油化工大學(xué)學(xué)報(bào);2011年02期

5 李露瑩;;考慮新用戶負(fù)荷發(fā)展的中短期負(fù)荷預(yù)測(cè)方法[J];供用電;2012年04期

6 王衍東,顧潔,胡斌;基于AHP的實(shí)用中長(zhǎng)期電力負(fù)荷預(yù)測(cè)綜合模型[J];華東電力;2005年01期

7 鄭志杰;李磊;趙蘭明;;考慮數(shù)據(jù)不確定性的中長(zhǎng)期電力負(fù)荷預(yù)測(cè)[J];電力系統(tǒng)保護(hù)與控制;2011年07期

8 李釗年;陶鳳玲;史旺旺;姜楠;李積花;倪三川;;基于譜分析與LS-SVM的中期電力負(fù)荷預(yù)測(cè)[J];電力系統(tǒng)保護(hù)與控制;2011年09期

9 朱斌;劉曉軍;;應(yīng)用人工神經(jīng)網(wǎng)絡(luò)算法進(jìn)行短期負(fù)荷預(yù)測(cè)[J];江蘇電機(jī)工程;2006年01期

10 江鑫;劉曉華;高榮;;基于改進(jìn)全局和聲搜索算法LSSVM的短期電力負(fù)荷預(yù)測(cè)[J];計(jì)算技術(shù)與自動(dòng)化;2012年02期

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