本文選題：關(guān)聯(lián)信息挖掘　切入點：熵相關(guān)系數(shù)　出處：《電力系統(tǒng)自動化》2017年03期 　論文類型：期刊論文

【摘要】：對風(fēng)電功率歷史數(shù)據(jù)進行關(guān)聯(lián)信息挖掘,將有助于提高短期風(fēng)電功率預(yù)測的準(zhǔn)確度和計算效率。為解決風(fēng)電功率預(yù)測模型的輸入、輸出變量的相關(guān)性冗余問題,嘗試采用了一種基于信息熵和互信息的熵相關(guān)系數(shù)指標(biāo),旨在量化評估不同歷史日風(fēng)電樣本與待預(yù)測日參考樣本間的復(fù)雜非線性映射關(guān)系,并與線性相關(guān)系數(shù)、秩相關(guān)系數(shù)、歐氏距離指標(biāo)進行了對比研究。同時,設(shè)計了一種BP神經(jīng)網(wǎng)絡(luò)改進模型,通過親密樣本篩選、隱含層結(jié)構(gòu)尋優(yōu)、網(wǎng)絡(luò)權(quán)重賦初值等環(huán)節(jié),克服了傳統(tǒng)預(yù)測模型的訓(xùn)練數(shù)據(jù)冗余度大、收斂速度慢問題,提高了預(yù)測模型的泛化能力和計算效率。對某風(fēng)電場實測數(shù)據(jù)的算例分析表明,所提出的方法在改善短期風(fēng)電功率預(yù)測性能方面具有應(yīng)用可行性。
[Abstract]:The association information mining of wind power historical data will help to improve the accuracy and computational efficiency of short-term wind power prediction. In order to solve the problem of input and output redundancy of wind power prediction model, An index of entropy correlation coefficient based on information entropy and mutual information is used to quantitatively evaluate the complex nonlinear mapping relationship between wind power samples and reference samples to be forecasted on different historical days, and to be related to linear correlation coefficient, rank correlation coefficient, linear correlation coefficient, rank correlation coefficient, linear correlation coefficient, rank correlation coefficient, linear correlation coefficient and rank correlation coefficient. The Euclidean distance index is compared and studied. At the same time, an improved BP neural network model is designed. Through the close sample selection, the hidden layer structure is optimized, the network weight is assigned initial value, and so on. It overcomes the problems of large redundancy of training data and slow convergence speed of the traditional prediction model, and improves the generalization ability and computational efficiency of the prediction model. The proposed method is feasible in improving the prediction performance of short-term wind power.
【作者單位】： 清華大學(xué)電機工程與應(yīng)用電子技術(shù)系;電力系統(tǒng)及發(fā)電設(shè)備控制和仿真國家重點實驗室清華大學(xué);國網(wǎng)吉林省電力有限公司;
【基金】：國家自然科學(xué)基金資助項目(51077078) 國家科技支撐計劃資助項目(2015BAA01B01)~~
【分類號】：TM614
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本文編號：1597816