發(fā)布時間：2018-03-19 16:51

  本文選題：臺區(qū)負(fù)荷預(yù)測　切入點(diǎn)：聚類　出處：《華北電力大學(xué)(北京)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著電力用電信息采集系統(tǒng)的優(yōu)化升級,電力用戶側(cè)除了能夠采集到傳統(tǒng)的電能使用信息外,也能獲得用戶電能質(zhì)量、96點(diǎn)負(fù)荷曲線、用電行為偏好等多維度反映用戶特性的數(shù)據(jù)。配電臺區(qū)負(fù)荷預(yù)測作為傳統(tǒng)負(fù)荷預(yù)測領(lǐng)域的細(xì)分,是進(jìn)行精細(xì)化的臺區(qū)用電管理、運(yùn)行調(diào)度與網(wǎng)架結(jié)構(gòu)優(yōu)化的新興手段。受制于臺區(qū)負(fù)荷的隨機(jī)性與多樣性,傳統(tǒng)預(yù)測方法在臺區(qū)級的預(yù)測中表現(xiàn)欠佳,而借助大數(shù)據(jù)平臺這一新興工具,對用戶側(cè)采集到的冗雜且大量的用戶特性信息加以利用,能夠有效提高臺區(qū)負(fù)荷預(yù)測的預(yù)測精度與預(yù)測適應(yīng)性。本文分析臺區(qū)負(fù)荷數(shù)據(jù)的特點(diǎn)及其關(guān)聯(lián)因素,借助大數(shù)據(jù)平臺中K-means、BIRCH、WARD等聚類算法,對上海市2977個臺區(qū)進(jìn)行聚類劃分,結(jié)合實(shí)際臺區(qū)行業(yè)構(gòu)成分析了各聚類簇的特征及劃分的有效性。聚類結(jié)果表明臺區(qū)內(nèi)用戶對氣象、經(jīng)濟(jì)等因素均有不同的敏感度,以聚類結(jié)果為依據(jù),對不同用電特性的用戶類分別建立預(yù)測模型是提高臺區(qū)負(fù)荷預(yù)測精度的有效手段。提出基于機(jī)器學(xué)習(xí)中的嶺回歸算法與自適應(yīng)思想的自適應(yīng)嶺回歸預(yù)測模型,依據(jù)自適應(yīng)程度將模型劃分為3種模式,對臺區(qū)負(fù)荷建模并實(shí)際訓(xùn)練、預(yù)測。3種模式在訓(xùn)練用時、預(yù)測精度、敏感度方面表現(xiàn)出不同特性,適用不同預(yù)測環(huán)境。在聚類與回歸模型構(gòu)建的基礎(chǔ)上,進(jìn)一步提出基于聚類及自適應(yīng)嶺回歸技術(shù)的臺區(qū)負(fù)荷預(yù)測方法,設(shè)計(jì)3種聚類特征選取方式與5種聚類算法作為預(yù)測自適應(yīng)優(yōu)化的可調(diào)模塊,增強(qiáng)了預(yù)測的動態(tài)優(yōu)化與誤差調(diào)控能力。使用該方法對上海市某包含487戶用戶的臺區(qū)進(jìn)行預(yù)測,算例結(jié)果顯示在不同預(yù)測環(huán)境中通過優(yōu)選聚類特征、聚類算法及模型參數(shù)自適應(yīng)調(diào)節(jié),該預(yù)測方法能達(dá)到較高的預(yù)測精度與環(huán)境適應(yīng)能力。
[Abstract]:With the optimization and upgrading of electric power information acquisition system, the power user side can not only collect the traditional power usage information, but also obtain the 96 point load curve of power quality. As a subdivision of traditional load forecasting field, distribution station area load forecasting is a fine management of station area power consumption. Due to the randomness and diversity of the load in Taiwan area, the traditional forecasting method is not good in the prediction of Taiwan district level, but with the help of big data platform, which is a new tool, It can effectively improve the forecasting accuracy and adaptability of station load forecasting by using the miscellaneous and large amount of user characteristic information collected by user side. This paper analyzes the characteristics of station load data and its related factors. With the help of K-means-BIRCHWARD and other clustering algorithms in big data platform, the cluster classification of 2977 stations in Shanghai is carried out, and the characteristics of each cluster and the validity of the division are analyzed in combination with the industry composition of the actual station. The clustering results show that the users in the station are sensitive to meteorology. Economic and other factors have different sensitivity, based on clustering results, It is an effective method to improve the accuracy of load forecasting for different users with different power consumption characteristics. An adaptive ridge regression prediction model based on machine learning and adaptive thinking is proposed. According to the adaptive degree, the model is divided into three models, and the load in the station area is modeled and trained in practice. The prediction of the three models shows different characteristics in training time, prediction accuracy and sensitivity. Based on the construction of clustering and regression models, a load forecasting method based on clustering and adaptive ridge regression is proposed. Three kinds of clustering feature selection methods and five clustering algorithms are designed as adjustable modules for adaptive predictive optimization, which enhances the dynamic optimization and error control ability of prediction. The proposed method is used to predict a certain station area in Shanghai, which includes 487 users. The simulation results show that the prediction method can achieve high prediction accuracy and environmental adaptability by optimizing clustering features, clustering algorithm and adaptive adjustment of model parameters in different prediction environments.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM715

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本文編號：1635149