采用最近歷史觀測值和PLSR進(jìn)行空間相關(guān)性超短期風(fēng)速預(yù)測
發(fā)布時間：2018-08-13 11:06
【摘要】：為提高超短期風(fēng)速預(yù)測的可靠性和準(zhǔn)確性,將被預(yù)測地點(本地)周邊測風(fēng)塔風(fēng)速風(fēng)向等當(dāng)前和最近歷史觀測值作為基礎(chǔ)數(shù)據(jù),采用空間相關(guān)性來預(yù)測本地的未來風(fēng)速。首先,依據(jù)風(fēng)向和風(fēng)速的延遲相關(guān)性,挑選出上游測風(fēng)塔。之后,結(jié)合最優(yōu)延遲時間,利用各上游和本地最近的風(fēng)速觀測值來訓(xùn)練預(yù)測模型。最后,將各上游風(fēng)速的當(dāng)前觀測值輸入模型,即可得到本地的風(fēng)速預(yù)測值。以偏最小二乘回歸(partial least squares regression,PLSR)為主要模型,并采用線性回歸(linear regression,LR)、最小二乘支持向量回歸等模型進(jìn)行對照。以冬季風(fēng)時期的荷蘭Huibertgat和天津為被預(yù)測地點,進(jìn)行了PLSR、LR預(yù)測誤差與模型階數(shù)、樣本容量之間關(guān)系的數(shù)值實驗。研究表明,在冬季風(fēng)時期,當(dāng)樣本容量達(dá)到一定程度后,預(yù)測誤差的變化對階數(shù)、樣本容量和模型的類型均不再敏感。這表明空間相關(guān)性是一種較為可靠的超短期風(fēng)速預(yù)測方法。
[Abstract]:In order to improve the reliability and accuracy of ultra-short term wind speed prediction, the current and recent historical observations such as wind speed direction of the wind tower around the predicted location (local) are taken as the basic data, and the spatial correlation is used to predict the local future wind speed. First, the upstream wind tower is selected according to the correlation between wind direction and wind speed delay. Then, combined with the optimal delay time, the prediction model is trained by using the most recent observations of the upstream and local wind speeds. Finally, the local wind speed prediction value can be obtained by inputting the current observation values of each upstream wind speed into the model. Partial least square regression (partial least squares regression) was used as the main model, and linear regression and least squares support vector regression (LS-SVM) were used as the control model. A numerical experiment on the relationship between the prediction error and the order of the model and the sample size was carried out using Huibertgat and Tianjin in winter monsoon period as the predicted sites. The results show that the variation of prediction error is not sensitive to order sample size and model type when the sample size reaches a certain level during winter monsoon period. This indicates that spatial correlation is a more reliable method for super-short-term wind speed prediction.
【作者單位】： 天津大學(xué)電氣與自動化工程學(xué)院;天津市過程檢測與控制重點實驗室(天津大學(xué));
【分類號】：TM614
，
本文編號：2180790
[Abstract]:In order to improve the reliability and accuracy of ultra-short term wind speed prediction, the current and recent historical observations such as wind speed direction of the wind tower around the predicted location (local) are taken as the basic data, and the spatial correlation is used to predict the local future wind speed. First, the upstream wind tower is selected according to the correlation between wind direction and wind speed delay. Then, combined with the optimal delay time, the prediction model is trained by using the most recent observations of the upstream and local wind speeds. Finally, the local wind speed prediction value can be obtained by inputting the current observation values of each upstream wind speed into the model. Partial least square regression (partial least squares regression) was used as the main model, and linear regression and least squares support vector regression (LS-SVM) were used as the control model. A numerical experiment on the relationship between the prediction error and the order of the model and the sample size was carried out using Huibertgat and Tianjin in winter monsoon period as the predicted sites. The results show that the variation of prediction error is not sensitive to order sample size and model type when the sample size reaches a certain level during winter monsoon period. This indicates that spatial correlation is a more reliable method for super-short-term wind speed prediction.
【作者單位】： 天津大學(xué)電氣與自動化工程學(xué)院;天津市過程檢測與控制重點實驗室(天津大學(xué));
【分類號】：TM614
，
本文編號：2180790
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