【摘要】：有效準確的電力負荷預測既是使電網(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心、最直接、最現(xiàn)實的用電問題提供了先決服務。因此，對該領域的研究一直是學術界的熱點問題。 支持向量機（Support Vector Machine，簡稱SVM）是一種新興的學習機器，具有較為完備的理論基礎和較好的學習性能，成功解決了神經(jīng)網(wǎng)絡難以克服的諸多問題，被稱為神經(jīng)網(wǎng)絡的替代算法。因此，本論文將其引入到電力系統(tǒng)的短期負荷預測中來。在研究中本文發(fā)現(xiàn)，負荷預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。在進行預測時，如果不對眾多因素（屬性)進行處理，勢必會提高預測模型的復雜程度并影響其實現(xiàn)效果，從而導致預測失準等問題。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)，導致一些有用的信息被去除，同樣會致使預測失準。 針對上述問題，本文進行了進一步研究。首先，采用粗糙集的有關理論與方法，對基于支持向量機的電力負荷預測技術進行改進，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測技術在預測效果上的差別，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確實取得了更加精確的預測效果。通過分析認為，其對解決電力負荷預測這一與企業(yè)管理者的決策息息相關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.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP181;F426.61

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本文編號：2265914