本文關(guān)鍵詞：基于數(shù)據(jù)挖掘與關(guān)聯(lián)規(guī)則的月度統(tǒng)計線損計算　出處：《燕山大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 供售電量不同期 LSSVM 售電量計算 關(guān)聯(lián)規(guī)則挖掘 統(tǒng)計線損率

【摘要】：目前,供售電量不同期問題在實際線損抄表制度中尤為突出,這使統(tǒng)計線損的計算結(jié)果與實際情況極為不符,同時造成統(tǒng)計線損率明顯虛增或虛降,導(dǎo)致統(tǒng)計線損與理論線損考核指標無法比對分析。因此,解決供售電量不同期問題對統(tǒng)計線損的計算具有重要意義。本文提出一種基于最小二乘支持向量機法(Least Squares Support Vector Machines,LSSVM)的實際月度售電量計算模型,基于此計算實際統(tǒng)計線損,有效解決了供售電量不同步的問題,使得統(tǒng)計線損與理論線損可以進行對比分析,為供電公司線損考核與管理工作提供有價值的參考。主要內(nèi)容如下:首先,研究并介紹了數(shù)據(jù)挖掘理論基本概念及算法,選用適合計算相鄰兩月不對應(yīng)區(qū)間售電量的LSSVM算法;概述了關(guān)聯(lián)規(guī)則挖掘理論及相關(guān)算法。其次,利用關(guān)聯(lián)規(guī)則相關(guān)理論挖掘隨機因素發(fā)生時長與售電量變化之間的關(guān)聯(lián)關(guān)系,并構(gòu)建經(jīng)濟隸屬度函數(shù)與變權(quán)重系數(shù),同時量化了單位時間內(nèi)隨機因素對售電量變化幅度的影響,對售電量計算結(jié)果進行修正,使其更加精確、全面。提出了基于LSSVM與關(guān)聯(lián)規(guī)則挖掘的月度統(tǒng)計線損計算方法,同時引入信息融合理論將低壓臺區(qū)用戶抄表數(shù)據(jù)信息融合,在等效抄表例日的基礎(chǔ)上,計算得到等效抄表區(qū)間,解決低壓用戶抄表不確定性與復(fù)雜性問題。最后,在初步計算結(jié)果的基礎(chǔ)上,利用經(jīng)濟因素及關(guān)聯(lián)規(guī)則挖掘結(jié)果對其進行多重修正,得到與供電量同期的月度售電量,從而得到月度實際統(tǒng)計線損。同時提出了基于關(guān)聯(lián)規(guī)則推薦的極限統(tǒng)計線損計算方法,通過計算相關(guān)系數(shù)尋找推薦模型的方法,擴大原有方法的使用范圍,提高計算實用性。通過仿真驗證并分析所提出方法的可行性與實用性。
[Abstract]:At present, the problem of different periods of electricity supply is particularly prominent in the actual line loss meter reading system, which makes the calculation results of the statistical line loss extremely inconsistent with the actual situation, at the same time, the statistical line loss rate obviously increases or falls. Statistical line loss and theoretical line loss assessment indicators can not be compared and analyzed. It is very important to solve the problem of different time period of electricity supply and sale to calculate the statistical line loss. In this paper, a new method based on least squares support vector machine (LS-SVM) is proposed. Least Squares Support Vector Machines. Based on the actual monthly sales calculation model of LSSVM, the actual statistical line loss is calculated, which effectively solves the problem that the electricity supply is out of sync, and makes the statistical line loss and the theoretical line loss can be compared and analyzed. The main contents are as follows: firstly, the basic concepts and algorithms of data mining theory are studied and introduced. Select the LSSVM algorithm which is suitable to calculate the electricity sales between the adjacent two months without corresponding interval. This paper summarizes the theory of association rules mining and related algorithms. Secondly, mining the association relationship between the length of time of random factors and the change of electricity sales by using the correlation theory of association rules. The economic membership function and variable weight coefficient are constructed, and the influence of random factors on the range of electricity sales in unit time is quantified, and the calculation results of electricity sales are revised to make it more accurate. In this paper, a monthly statistical line loss calculation method based on LSSVM and association rule mining is proposed. At the same time, the information fusion theory is introduced to fuse the data of users' meter reading in low-voltage stations, on the basis of equivalent meter reading examples. The equivalent meter reading interval is obtained to solve the uncertainty and complexity problem of low voltage users meter reading. Finally, based on the preliminary calculation results, the economic factors and association rules mining results are used to modify the data. At the same time, we get the monthly sales of electricity and get the monthly actual line loss. At the same time, we put forward the limit statistical line loss calculation method based on association rules. The method of finding the recommended model by calculating the correlation coefficient expands the scope of use of the original method and improves the practicability of calculation. The feasibility and practicability of the proposed method are verified and analyzed by simulation.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TM744;TP311.13

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本文編號：1439830