基于支持向量機(jī)的風(fēng)電場風(fēng)速預(yù)測方法研究
發(fā)布時(shí)間：2018-11-25 21:31
【摘要】：能源枯竭、資源匱乏已經(jīng)成為一個(gè)全球性問題,從而可再生能源的開發(fā)與持續(xù)利用日益受到關(guān)注。其中,風(fēng)能作為潔凈、無污染和易于利用的可再生能源之一,更是在全世界范圍內(nèi)得到了廣泛的應(yīng)用。然而,由于風(fēng)電發(fā)電自身的波動(dòng)性和不穩(wěn)定性,給電力系統(tǒng)的安全穩(wěn)定和產(chǎn)能質(zhì)量造成了不良的影響。解決這些問題的關(guān)鍵在于,對風(fēng)電場風(fēng)速和風(fēng)電功率進(jìn)行預(yù)測。通過對風(fēng)電場風(fēng)速的準(zhǔn)確預(yù)測,可以降低風(fēng)電功率的隨機(jī)性,可有效緩解風(fēng)速變化對電力系統(tǒng)造成的不利影響。風(fēng)速預(yù)測方法近年來發(fā)展迅速,但是在預(yù)測方法的多元化和預(yù)測精度方面還有比較大的上升空間。本文通過建立和評估多種短期風(fēng)速預(yù)測模型,發(fā)現(xiàn)單一的風(fēng)速預(yù)測方法的預(yù)測精度相對不足,于是提出組合預(yù)測模型,并通過對對支持向量機(jī)方法的改進(jìn)和研究,綜合利用各風(fēng)速預(yù)測算法的優(yōu)點(diǎn),提出了一種基于最小二乘支持向量機(jī)的組合預(yù)測模型。該預(yù)測方法首先利用模糊層次分析法,在若干單項(xiàng)預(yù)測模型中篩選出灰色預(yù)測算法,人工神經(jīng)網(wǎng)絡(luò)預(yù)測算法和時(shí)間序列—卡爾曼濾波混合算法;然后以這三種單項(xiàng)預(yù)測模型作為輸入,并以實(shí)際風(fēng)速值作為輸出,進(jìn)行訓(xùn)練最小二乘支持向量機(jī);最終得出預(yù)測函數(shù)。本文還分別建立等權(quán)平均組合預(yù)測模型和最優(yōu)加權(quán)組合預(yù)測模型,且以這兩種組合預(yù)測模型為參照,來分析基于最小二乘支持向量機(jī)的組合預(yù)測模型的預(yù)測性能。本研究中,針對各模型的預(yù)測性能,采用預(yù)測平均絕對誤差,平均絕對百分比誤差以及誤差平方和,這三個(gè)誤差指標(biāo)來比較分析。通過以內(nèi)蒙古某風(fēng)電場計(jì)算出的小時(shí)風(fēng)速數(shù)據(jù)作為研究樣本,運(yùn)用MATLAB進(jìn)行仿真,采用各模型對風(fēng)速進(jìn)行短期預(yù)測,驗(yàn)證了基于最小二乘支持向量機(jī)的風(fēng)速組合預(yù)測模型的有效性。仿真試驗(yàn)表明,本文提出的基于支持向量機(jī)組合預(yù)測方法模型可進(jìn)一步提升風(fēng)速預(yù)測精度,而且相較于傳統(tǒng)的兩種組合預(yù)測模型,也具有比較大的精度優(yōu)勢。
[Abstract]:Energy depletion and resource scarcity have become a global problem, so the development and sustainable utilization of renewable energy have been paid more and more attention. Among them, wind energy, as one of the clean, pollution-free and easy to use renewable energy, has been widely used in the world. However, due to the volatility and instability of wind power generation itself, the safety and stability of power system and the quality of production capacity are adversely affected. The key to solve these problems is to predict wind speed and power of wind farm. Through the accurate prediction of wind speed in wind farm, the randomness of wind power can be reduced, and the adverse effect of wind speed variation on power system can be effectively alleviated. Wind speed forecasting methods have been developed rapidly in recent years, but there is still much room for improvement in the diversification of forecasting methods and prediction accuracy. Through the establishment and evaluation of several short-term wind speed forecasting models, it is found that the prediction accuracy of a single wind speed forecasting method is relatively insufficient, so a combined forecasting model is proposed, and the support vector machine method is improved and studied. A combined prediction model based on least squares support vector machine (LS-SVM) is proposed by synthesizing the advantages of wind speed prediction algorithms. In this method, the grey prediction algorithm, the artificial neural network prediction algorithm and the hybrid time series Kalman filter algorithm are selected from several single prediction models by using the fuzzy analytic hierarchy process (FAHP). Then the three single prediction models are taken as input and the actual wind speed is taken as the output to train the least squares support vector machine (LS-SVM) and finally the prediction function is obtained. This paper also establishes the equal weight average combination prediction model and the optimal weighted combination forecast model, and takes these two combination forecast models as the reference to analyze the prediction performance of the combination forecast model based on the least square support vector machine (LS-SVM). In this study, the average absolute error, average absolute percentage error and sum of error square are used to compare and analyze the prediction performance of each model. By taking the hourly wind speed data calculated from a wind farm in Inner Mongolia as the research sample, MATLAB is used to simulate the wind speed, and each model is used to predict the wind speed in the short term. The validity of the wind speed combination prediction model based on least squares support vector machine is verified. The simulation results show that the proposed combined forecasting model based on support vector machine can further improve the accuracy of wind speed prediction, and it also has a large accuracy advantage compared with the traditional two combined forecasting models.
【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】：TP18;TM614
[Abstract]:Energy depletion and resource scarcity have become a global problem, so the development and sustainable utilization of renewable energy have been paid more and more attention. Among them, wind energy, as one of the clean, pollution-free and easy to use renewable energy, has been widely used in the world. However, due to the volatility and instability of wind power generation itself, the safety and stability of power system and the quality of production capacity are adversely affected. The key to solve these problems is to predict wind speed and power of wind farm. Through the accurate prediction of wind speed in wind farm, the randomness of wind power can be reduced, and the adverse effect of wind speed variation on power system can be effectively alleviated. Wind speed forecasting methods have been developed rapidly in recent years, but there is still much room for improvement in the diversification of forecasting methods and prediction accuracy. Through the establishment and evaluation of several short-term wind speed forecasting models, it is found that the prediction accuracy of a single wind speed forecasting method is relatively insufficient, so a combined forecasting model is proposed, and the support vector machine method is improved and studied. A combined prediction model based on least squares support vector machine (LS-SVM) is proposed by synthesizing the advantages of wind speed prediction algorithms. In this method, the grey prediction algorithm, the artificial neural network prediction algorithm and the hybrid time series Kalman filter algorithm are selected from several single prediction models by using the fuzzy analytic hierarchy process (FAHP). Then the three single prediction models are taken as input and the actual wind speed is taken as the output to train the least squares support vector machine (LS-SVM) and finally the prediction function is obtained. This paper also establishes the equal weight average combination prediction model and the optimal weighted combination forecast model, and takes these two combination forecast models as the reference to analyze the prediction performance of the combination forecast model based on the least square support vector machine (LS-SVM). In this study, the average absolute error, average absolute percentage error and sum of error square are used to compare and analyze the prediction performance of each model. By taking the hourly wind speed data calculated from a wind farm in Inner Mongolia as the research sample, MATLAB is used to simulate the wind speed, and each model is used to predict the wind speed in the short term. The validity of the wind speed combination prediction model based on least squares support vector machine is verified. The simulation results show that the proposed combined forecasting model based on support vector machine can further improve the accuracy of wind speed prediction, and it also has a large accuracy advantage compared with the traditional two combined forecasting models.
【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】：TP18;TM614
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 張濤;張明輝;李清偉;張sソ，
本文編號：2357432
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