【摘要】：有效準(zhǔn)確的電力負荷預(yù)測既是使電網(wǎng)安全、經(jīng)濟運行的有力保障，也為切實解決人民群眾最關(guān)心、最直接、最現(xiàn)實的用電問題提供了先決服務(wù)。因此，對該領(lǐng)域的研究一直是學(xué)術(shù)界的熱點問題。 支持向量機（Support Vector Machine，簡稱SVM）是一種新興的學(xué)習(xí)機器，具有較為完備的理論基礎(chǔ)和較好的學(xué)習(xí)性能，成功解決了神經(jīng)網(wǎng)絡(luò)難以克服的諸多問題，被稱為神經(jīng)網(wǎng)絡(luò)的替代算法。因此，本論文將其引入到電力系統(tǒng)的短期負荷預(yù)測中來。在研究中本文發(fā)現(xiàn)，負荷預(yù)測的影響因素有很多，有些因素是可以在特定情況下被去除的。在進行預(yù)測時，如果不對眾多因素（屬性)進行處理，勢必會提高預(yù)測模型的復(fù)雜程度并影響其實現(xiàn)效果，從而導(dǎo)致預(yù)測失準(zhǔn)等問題。若僅憑經(jīng)驗來對各屬性進行約減與提取，則又會因為缺乏依據(jù)，導(dǎo)致一些有用的信息被去除，同樣會致使預(yù)測失準(zhǔn)。 針對上述問題，本文進行了進一步研究。首先，采用粗糙集的有關(guān)理論與方法，對基于支持向量機的電力負荷預(yù)測技術(shù)進行改進，通過屬性約減與特征提取等工作，使得有用的信息被完整保留，，無用的信息被基本剔除，在最大限度上減少了外界不良因素對負荷預(yù)測系統(tǒng)的干擾。其次，進行算例分析與效果比較，對照改進前后的負荷預(yù)測技術(shù)在預(yù)測效果上的差別，從而驗證改進方案的有效性與可行性。通過驗證發(fā)現(xiàn)，上述改進所得到的新技術(shù)確實取得了更加精確的預(yù)測效果。通過分析認為，其對解決電力負荷預(yù)測這一與企業(yè)管理者的決策息息相關(guān)的熱點問題又提供了一套更加合理的方案。
[Abstract]:Effective and accurate power load forecasting not only ensures the security and economic operation of the power grid, but also provides a preliminary service for solving the most concerned, direct and realistic problems of electricity consumption among the people. Therefore, the research in this field has been a hot topic in academic circles. Support Vector Machine (Support Vector Machine,) is a new learning machine with relatively complete theoretical foundation and better learning performance. It has successfully solved many problems that can not be overcome by neural network and is called the substitute algorithm of neural network. Therefore, this paper introduces it into short-term load forecasting of power system. In this paper, it is found that there are many factors affecting load forecasting, and some factors can be removed under certain circumstances. In forecasting, if many factors (attributes) are not dealt with, the complexity of the prediction model will be increased and the effect of its implementation will be affected, which will lead to the misalignment of prediction and other problems. If each attribute is reduced and extracted only by experience, some useful information will be removed because of lack of basis, and the prediction will also be inaccurate. In view of the above problems, this paper has carried on the further research. Firstly, the theory and method of rough set are used to improve the power load forecasting technology based on support vector machine. Through attribute reduction and feature extraction, the useful information is preserved completely. Useless information is basically eliminated, which minimizes the interference of external adverse factors to the load forecasting system. Secondly, an example analysis and effect comparison are carried out to verify the effectiveness and feasibility of the improved method by comparing the difference of forecasting effect between before and after the improved load forecasting technology. Through verification, it is found that the new technique obtained by the above improvements has achieved a more accurate prediction effect. Through the analysis, it provides a more reasonable scheme for solving the hot problem of power load forecasting, which is closely related to the decision of enterprise managers.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP181;F426.61

【參考文獻】

中國期刊全文數(shù)據(jù)庫 前10條

1 康重慶,夏清,張伯明;電力系統(tǒng)負荷預(yù)測研究綜述與發(fā)展方向的探討[J];電力系統(tǒng)自動化;2004年17期

2 張林,劉先珊,陰和俊;基于時間序列的支持向量機在負荷預(yù)測中的應(yīng)用[J];電網(wǎng)技術(shù);2004年19期

3 吳宏曉,侯志儉;基于免疫支持向量機方法的電力系統(tǒng)短期負荷預(yù)測[J];電網(wǎng)技術(shù);2004年23期

4 楊延西,劉丁;基于小波變換和最小二乘支持向量機的短期電力負荷預(yù)測[J];電網(wǎng)技術(shù);2005年13期

5 陸建宇;王亮;王強;吳江;劉涌;;華東電網(wǎng)氣象負荷特性分析[J];華東電力;2006年11期

6 楊鏡非,謝宏,程浩忠;SVM與Fourier算法在電網(wǎng)短期負荷預(yù)測中的應(yīng)用[J];繼電器;2004年04期

7 祝志慧;孫云蓮;季宇;;基于經(jīng)驗?zāi)Ｊ椒纸夂妥钚《酥С窒蛄繖C的短期負荷預(yù)測[J];繼電器;2007年08期

8 吳軍基,倪黔東,孟紹良,劉皓明;基于人工神經(jīng)網(wǎng)絡(luò)的日負荷預(yù)測方法的研究[J];繼電器;1999年03期

9 張學(xué)工;關(guān)于統(tǒng)計學(xué)習(xí)理論與支持向量機[J];自動化學(xué)報;2000年01期

10 杜京義;侯媛彬;;基于遺傳算法的支持向量回歸機參數(shù)選取[J];系統(tǒng)工程與電子技術(shù);2006年09期

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