本文選題：配用電大數(shù)據(jù) + 用電量預(yù)測��； 參考：《電力系統(tǒng)保護(hù)與控制》2017年16期

【摘要】：用戶用電量的精準(zhǔn)預(yù)測是智能配用電大數(shù)據(jù)應(yīng)用和發(fā)展的關(guān)鍵之一。區(qū)別于傳統(tǒng)的基于行業(yè)分類的預(yù)測辦法,提出基于大數(shù)據(jù)挖掘技術(shù)的用戶用電多維度特征識(shí)別,以及在此基礎(chǔ)上的精準(zhǔn)用電量預(yù)測方法。基于海量多用戶用電特性,建立多維度用電特征評(píng)價(jià)指標(biāo)體系。對用戶用電特性空間進(jìn)行聚類和分析,挖掘和識(shí)別用電模式。在不同的用電模式下,分別建立用電量時(shí)間序列預(yù)測模型,避免用電模式差異對預(yù)測算法準(zhǔn)確性造成的不利影響。該方法適用于大數(shù)據(jù)平臺(tái)的分析與處理,算例分析結(jié)果表明其相比以往方法能顯著提高預(yù)測精度和穩(wěn)定性。
[Abstract]:The accurate prediction of user's electricity consumption is one of the keys to the application and development of intelligent distribution TV university data.Different from the traditional forecasting method based on industry classification, this paper proposes a multi-dimensional feature recognition method based on big data mining technology, and an accurate power consumption forecasting method based on it.Based on the massive and multi-user power consumption characteristics, a multi-dimensional power consumption evaluation index system is established.Cluster and analyze the user's power characteristic space, and mine and identify the power consumption pattern.In order to avoid the adverse influence of the difference of power consumption mode on the accuracy of prediction algorithm, the forecasting model of time series of electricity consumption is established under different power consumption modes.This method is suitable for the analysis and processing of big data platform. The result of example analysis shows that this method can improve the prediction accuracy and stability significantly compared with the previous method.
【作者單位】： 華中科技大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;廣東海洋大學(xué)數(shù)學(xué)與計(jì)算機(jī)學(xué)院;廣東省大數(shù)據(jù)分析與處理重點(diǎn)實(shí)驗(yàn)室;國電江蘇電力有限公司;遠(yuǎn)光軟件股份有限公司;
【基金】：廣東省重大科技專項(xiàng)(2014B010117006) 廣東省大數(shù)據(jù)分析與處理重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(2017005)
【分類號(hào)】：TM715;TP311.13
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本文編號(hào)：1745345