【摘要】：為了提高電力負(fù)荷預(yù)測(cè)精度,應(yīng)對(duì)電力系統(tǒng)智能化所帶來的數(shù)據(jù)海量化高維化帶來的單機(jī)計(jì)算資源不足的挑戰(zhàn),提出了一種在線序列優(yōu)化的極限學(xué)習(xí)機(jī)短期電力負(fù)荷預(yù)測(cè)模型。針對(duì)電力負(fù)荷數(shù)據(jù)特性,對(duì)極限學(xué)習(xí)機(jī)預(yù)測(cè)算法進(jìn)行在線序列優(yōu)化;引入分布式和multi-agent思想,提升負(fù)荷預(yù)測(cè)算法預(yù)測(cè)準(zhǔn)確率;采用云計(jì)算的MapReduce編程框架對(duì)提出的算法模型進(jìn)行并行化改進(jìn),提高其處理海量高維數(shù)據(jù)的能力。選用EUNITE提供的真實(shí)電力負(fù)荷數(shù)據(jù)進(jìn)行算例分析,在32節(jié)點(diǎn)云計(jì)算集群上進(jìn)行實(shí)驗(yàn),結(jié)果表明基于該模型的負(fù)荷預(yù)測(cè)精度均優(yōu)于傳統(tǒng)支持向量回歸預(yù)測(cè)算法和泛化神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)算法,且提出的算法具有優(yōu)異的并行性能。
[Abstract]:In order to improve the precision of power load forecasting and to meet the challenge of the shortage of single computer computing resources brought by the large amount of data brought by the intelligentization of the power system, a short-term power load forecasting model of the online sequence optimized limit learning machine is put forward. The limit learning machine prediction algorithm is online based on the characteristics of the power load data. Sequence optimization; introducing the distributed and multi-agent ideas to improve the prediction accuracy of the load forecasting algorithm; using the MapReduce programming framework of the cloud computing to improve the proposed algorithm model and improve its ability to deal with massive and high dimensional data. Use the real electrical load data provided by EUNITE to carry out an example analysis, in the 32 node cloud meter. The experimental results show that the load forecasting accuracy based on this model is better than the traditional support vector regression prediction algorithm and the generalization neural network prediction algorithm, and the proposed algorithm has excellent parallel performance.
【作者單位】： 華北電力大學(xué)控制與計(jì)算機(jī)工程學(xué)院;
【基金】：河北省科學(xué)研究項(xiàng)目(Z2012077,Z2010290)
【分類號(hào)】：TM715.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王奔;冷北雪;張喜海;單纕，

本文編號(hào)：2121390