發(fā)布時(shí)間：2018-04-11 07:00

  本文選題：空間負(fù)荷預(yù)測(cè) + 負(fù)荷密度指標(biāo)法　； 參考：《電力系統(tǒng)自動(dòng)化》2017年03期

【摘要】：針對(duì)基于智能算法的負(fù)荷密度指標(biāo)法對(duì)樣本依賴性強(qiáng)且在各地實(shí)際應(yīng)用困難的不足,提出一種考慮地域差異的配電網(wǎng)空間負(fù)荷聚類及一體化預(yù)測(cè)方法。該方法首先通過(guò)大量調(diào)研得到分布在不同地區(qū)、分屬不同類型的負(fù)荷樣本及所處地區(qū)信息;然后利用基于日負(fù)荷曲線的負(fù)荷分類校驗(yàn)及精選方法對(duì)所有調(diào)研樣本進(jìn)行分類精選;再根據(jù)區(qū)域分類、負(fù)荷分類對(duì)精選樣本構(gòu)成的全樣本空間進(jìn)行兩級(jí)劃分,得到分層級(jí)子樣本空間;最后根據(jù)待測(cè)地塊的屬性信息對(duì)子樣本空間進(jìn)行匹配,選取與其最相似的子樣本空間作為訓(xùn)練樣本,構(gòu)建支持向量機(jī)模型預(yù)測(cè)各地塊的負(fù)荷密度,進(jìn)而得到電力負(fù)荷的空間分布。工程實(shí)例分析表明了該方法的實(shí)用性和有效性。
[Abstract]:In view of the shortage of intelligent algorithm based load density index method which is highly dependent on samples and difficult to be applied in various places, a method of spatial load clustering and integrated forecasting considering regional differences is proposed.Firstly, the load samples distributed in different regions and different types of load samples are obtained by a large number of investigations, and then the load classification checking and selecting method based on daily load curve is used to classify and select all the investigation samples.Then according to regional classification and load classification, the whole sample space of selected samples is divided into two levels, and the sub-sample space is obtained. Finally, the sub-sample space is matched according to the attribute information of the plots to be measured.The most similar subsample space is chosen as the training sample, and the support vector machine model is constructed to predict the load density of each plot, and then the spatial distribution of power load is obtained.An engineering example shows the practicability and effectiveness of the method.
【作者單位】： 浙江大學(xué)電氣工程學(xué)院;國(guó)網(wǎng)浙江省電力公司經(jīng)濟(jì)技術(shù)研究院;
【基金】：國(guó)家電網(wǎng)公司科技項(xiàng)目(5211JY150016)~~
【分類號(hào)】：TM715

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本文編號(hào)：1734911