本文關(guān)鍵詞：基于WRF模式和PSO-LSSVM的風(fēng)電場短期風(fēng)速訂正　出處：《電力系統(tǒng)保護(hù)與控制》2017年22期 　論文類型：期刊論文

  更多相關(guān)文章： 風(fēng)力發(fā)電 風(fēng)速訂正 WRF模式 PSO-LSSVM 預(yù)測效果

【摘要】：風(fēng)速預(yù)測是風(fēng)電場風(fēng)電功率預(yù)測的基礎(chǔ)與前提,以數(shù)值天氣預(yù)報(bào)(WRF模式)為基礎(chǔ)進(jìn)行風(fēng)速預(yù)測,為了提高WRF模式預(yù)測的準(zhǔn)確性,采用最小二乘支持向量機(jī)(Least Squares Support Vector Machine,LSSVM)對WRF模式輸出的風(fēng)速進(jìn)行訂正。同時(shí),為提高LSSVM算法的精確度和減小擬合過程的復(fù)雜度,采用粒子群優(yōu)化算法(Particle Swarm Optimization,PSO)對其參數(shù)進(jìn)行優(yōu)化。試驗(yàn)結(jié)果表明:采用LSSVM訂正可以進(jìn)一步減小WRF模式預(yù)測風(fēng)速的誤差,再經(jīng)過PSO優(yōu)化后,相對均方根誤差和相對平均絕對誤差降低了5%~10%,均方根誤差下降了0.5 m/s。與未經(jīng)優(yōu)化的LSSVM以及極限學(xué)習(xí)機(jī)(ELM)算法對比分析后得出,粒子群優(yōu)化最小二乘支持向量機(jī)(PSO-LSSVM)對WRF模式預(yù)測的風(fēng)速有較好的訂正效果,能進(jìn)一步提高風(fēng)速預(yù)測的準(zhǔn)確性。
[Abstract]:Wind speed prediction is the basis and premise of wind power prediction in wind farm. In order to improve the accuracy of WRF model, wind speed prediction is carried out on the basis of numerical weather forecast. The least square support vector machine (LS-SVM) is used for least Squares Support Vector Machine. In order to improve the accuracy of the LSSVM algorithm and reduce the complexity of the fitting process, the wind speed of WRF mode is revised by LSSVM. Particle Swarm Optimization (PSO) algorithm is adopted. The experimental results show that the LSSVM correction can further reduce the error of WRF model in predicting wind speed, and then after PSO optimization. The relative root mean square error and the relative mean absolute error are reduced by 5% and 10% respectively. The root mean square error (RMS) decreased by 0. 5 m / s. The results were compared with the unoptimized LSSVM and LLM algorithm. Particle swarm optimization (PSO) least squares support vector machine (LSSVM) has a good effect on the wind speed prediction of WRF model, and can further improve the accuracy of wind speed prediction.
【作者單位】： 南京信息工程大學(xué)信息與控制學(xué)院;南京信息工程大學(xué)氣象災(zāi)害預(yù)報(bào)預(yù)警與評估協(xié)同創(chuàng)新中心;
【基金】：國家自然科學(xué)基金項(xiàng)目(41675156) 國家公益性行業(yè)(氣象)科研專項(xiàng)(GYHY20110604) 江蘇省六大人才高峰項(xiàng)目(WLW-021)資助 江蘇省研究生創(chuàng)新工程省立項(xiàng)目(SJZZ16_0155)~~
【分類號】：TM614
【正文快照】： This work is supported by National Natural Science Foundation of China(No.41675156).隨著人類對能源需求的不斷增加,傳統(tǒng)的煤、石油、天然氣等化石能源被大量開采,儲量大幅度下降,同時(shí)化石能源的過度使用也造成了溫室效應(yīng)、環(huán)境污染等問題,所以清潔能源的開發(fā)和使用迫在

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