發(fā)布時(shí)間：2018-12-25 15:34

【摘要】：售電量是電網(wǎng)企業(yè)重要的經(jīng)濟(jì)考核指標(biāo),月度售電量預(yù)測(cè)工作是電網(wǎng)企業(yè)營(yíng)銷(xiāo)部門(mén)一項(xiàng)重要的日常工作,準(zhǔn)確的進(jìn)行月度售電量預(yù)測(cè)可以為電網(wǎng)企業(yè)提供營(yíng)銷(xiāo)決策支持,對(duì)制定增供擴(kuò)銷(xiāo)計(jì)劃、開(kāi)展電能替代、實(shí)施有序用電方案、提升客戶服務(wù)品質(zhì)等具有重要意義。目前各電網(wǎng)企業(yè)月度售電量預(yù)測(cè)多采用對(duì)比分析、結(jié)構(gòu)分析、回歸分析、神經(jīng)網(wǎng)絡(luò)等方法。這些方法可在一定程度上對(duì)售電量進(jìn)行預(yù)測(cè),但對(duì)國(guó)家電網(wǎng)公司整體售電量預(yù)測(cè)精度并不是很理想,其主要原因是沒(méi)有考慮國(guó)家電網(wǎng)公司各地區(qū)售電量曲線的不同特征,只利用一種預(yù)測(cè)算法對(duì)多地區(qū)的售電量進(jìn)行預(yù)測(cè),這樣必然會(huì)導(dǎo)致預(yù)測(cè)精度不高。為解決上述問(wèn)題,本文提出了兩種方法。一種是基于歷史曲線的售電量預(yù)測(cè)方法。根據(jù)國(guó)家電網(wǎng)公司及下屬27家省(市)公司售電量曲線在時(shí)域和頻域下的特征,對(duì)27家省(市)公司進(jìn)行聚類(lèi)。對(duì)不同類(lèi)別的省(市)公司,根據(jù)售電量曲線特征與預(yù)測(cè)算法(SVM回歸、BP神經(jīng)網(wǎng)絡(luò)、ARIMA等)的適配性,選擇相應(yīng)的預(yù)測(cè)方法,對(duì)同一類(lèi)別內(nèi)的省(市)公司采用同一種預(yù)測(cè)算法。在基于歷史曲線的售電量預(yù)測(cè)的基礎(chǔ)上,本文將天氣、經(jīng)濟(jì)、節(jié)假日和社會(huì)事件等影響因素納入考慮,建立基于SVM回歸的售電量預(yù)測(cè)修正模型,根據(jù)影響因素的月度售電量預(yù)測(cè)修正模型,進(jìn)一步提高預(yù)測(cè)精度。另一種方法是考慮春節(jié)因素的售電量調(diào)整方法,該方法首先利用歷史年第一季度每月售電量占季度比重和第一季度每月1日距離當(dāng)年春節(jié)的天數(shù)建立函數(shù)關(guān)系,天數(shù)為輸入,占季度比為輸出,利用得到的一元函數(shù)預(yù)測(cè)1、2、3月份售電量占季度比,進(jìn)而根據(jù)預(yù)測(cè)的占季度比及調(diào)整前售電量預(yù)測(cè)值得到基于春節(jié)因素調(diào)整后1、2、3月份預(yù)測(cè)值。利用本文的預(yù)測(cè)方法,以國(guó)家電網(wǎng)公司2010年至2014年的售電量數(shù)據(jù)作為歷史數(shù)據(jù),對(duì)國(guó)家電網(wǎng)公司2015年每月的售電量進(jìn)行預(yù)測(cè),然后和實(shí)際的2015年售電量比較,預(yù)測(cè)平均誤差為1.78%,結(jié)果表明,本文提出的售電量預(yù)測(cè)方法可靠,有效,且精度較高。
[Abstract]:Electricity sales is an important economic assessment index for power grid enterprises. Monthly electricity sales forecasting is an important daily work of power grid enterprise marketing department. Accurate monthly electricity sales prediction can provide marketing decision support for power grid enterprises. It is of great significance to make the plan of increasing supply and expanding sales, to carry out electric energy substitution, to carry out orderly power consumption scheme, and to improve the quality of customer service. At present, the monthly electricity sales forecast of power grid enterprises mostly adopts the methods of comparative analysis, structure analysis, regression analysis, neural network and so on. These methods can be used to predict the electricity sales to a certain extent, but the accuracy of the overall electricity sales prediction of the State Grid Company is not very good. The main reason is that the different characteristics of the electricity sales curves in the various regions of the State Grid Company are not considered. Only one prediction algorithm is used to predict the electricity sales in many areas, which will inevitably lead to the low accuracy of the prediction. In order to solve the above problems, two methods are proposed in this paper. One is the forecasting method of electricity sales based on historical curve. According to the characteristics of the electricity sales curve of the State Grid Company and 27 provincial (municipal) companies in the time domain and the frequency domain, 27 provincial (municipal) companies were clustered. According to the characteristics of the sales curve and the adaptability of the prediction algorithm (SVM regression, BP neural network, ARIMA etc.), the corresponding forecasting methods are selected for different kinds of provincial (municipal) companies. The same prediction algorithm is used for provincial (municipal) companies in the same category. On the basis of forecasting electricity sales based on historical curve, this paper takes weather, economy, holidays and social events into account, and establishes a revised model of electricity sales forecasting based on SVM regression. The forecast accuracy is further improved according to the monthly electricity sales forecast correction model based on the influencing factors. Another method is to take into account the factors of the Spring Festival to adjust electricity sales. Firstly, the method uses the proportion of electricity sales per month in the first quarter of a historical year and the number of days between the first quarter and the Spring Festival in the first quarter to establish a functional relationship. The number of days is input. The ratio of quarter to quarter is output, and the one-variable function is used to forecast the quarterly ratio of electricity sales in March, and then according to the predicted quarterly ratio and the forecast value of electricity sales before adjustment, the forecast value for March is based on the adjustment of Spring Festival factor. Using the forecasting method of this paper, taking the electricity sales data of State Grid Company from 2010 to 2014 as historical data, this paper forecasts the monthly electricity sales of State Grid Company in 2015, and then compares with the actual electricity sales in 2015. The average error of prediction is 1.78. The results show that the method proposed in this paper is reliable, effective and accurate.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM715

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 顏偉;程超;薛斌;李丹;陳飛;王順昌;;結(jié)合X12乘法模型和ARIMA模型的月售電量預(yù)測(cè)方法[J];電力系統(tǒng)及其自動(dòng)化學(xué)報(bào);2016年05期

2 李英惠;胥超;;基于時(shí)間序列模型的售電量預(yù)測(cè)方法[J];山東電力技術(shù);2014年06期

3 王偉;楊輝華;劉振丙;李靈巧;;基于極限學(xué)習(xí)機(jī)的短期電力負(fù)荷預(yù)測(cè)[J];計(jì)算機(jī)仿真;2014年04期

4 王奕萱;;基于統(tǒng)計(jì)特征量和支持向量機(jī)的短期售電量預(yù)測(cè)研究[J];華北電力技術(shù);2013年12期

5 潘小輝;劉麗萍;李揚(yáng);;提高月度售電量預(yù)測(cè)精度的一種新方法[J];電力需求側(cè)管理;2013年03期

6 段樹(shù)喬;段方婕;;基于多項(xiàng)式回歸函數(shù)的電網(wǎng)公司售電量預(yù)測(cè)方法[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2012年18期

7 楊云瑩;任玉瓏;段鍇;;基于虛擬變量與時(shí)間序列法的電量需求預(yù)測(cè)[J];電力需求側(cè)管理;2011年05期

8 曹葵康;沈海斌;;基于多樣本的在線支持向量回歸算法[J];浙江大學(xué)學(xué)報(bào)(理學(xué)版);2011年04期

9 張士強(qiáng);王雯;王健;;ARIMA模型在城市年用電量預(yù)測(cè)中的應(yīng)用[J];電力需求側(cè)管理;2010年06期

10 汪滔;張偉;呂萌明;汪l，

本文編號(hào)：2391306