本文關(guān)鍵詞： 風(fēng)力發(fā)電 風(fēng)速預(yù)測 時間序列 Elman神經(jīng)網(wǎng)絡(luò) BP神經(jīng)網(wǎng)絡(luò) 組合模型　出處：《華北電力大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：風(fēng)能以其可再生、無污染的特性越來越受到人們的關(guān)注。但由于風(fēng)速的波動性和隨機性,風(fēng)機出力很不穩(wěn)定。隨著風(fēng)力發(fā)電在電網(wǎng)中所占的比重逐漸增加,其對電力系統(tǒng)的安全穩(wěn)定運行一定會造成一些不利影響。風(fēng)電并網(wǎng)及電力調(diào)度中,風(fēng)速預(yù)測的準(zhǔn)確性可以提供非常重要的參考,大大的消除風(fēng)速波動對電網(wǎng)的影響。鑒于這些原因,對風(fēng)電場風(fēng)速進行預(yù)測研究是非常有意義的。 本文針對風(fēng)速數(shù)據(jù)的非線性特性,利用改進的Elman神經(jīng)網(wǎng)絡(luò)修正ARIMA模型預(yù)測結(jié)果的方法,運用時間序列與神經(jīng)網(wǎng)絡(luò)的組合模型對短期風(fēng)速預(yù)測進行研究。先利用ARIMA模型對風(fēng)速進行預(yù)測,其線性規(guī)律信息包含在時間序列預(yù)測結(jié)果中,非線性規(guī)律包含在預(yù)測誤差中。再將ARIMA模型的預(yù)測誤差及歷史風(fēng)速一階差分序列作為改進的Elman神經(jīng)網(wǎng)絡(luò)輸入變量,將ARIMA模型的風(fēng)速預(yù)測誤差作為輸出變量。最后將ARIMA模型預(yù)測結(jié)果與Elman神經(jīng)網(wǎng)絡(luò)的誤差預(yù)測結(jié)果疊加,得到最終修正后的預(yù)測風(fēng)速。 為證明方法的有效性,分別與單一ARIMA模型、ARIMA-BP神經(jīng)網(wǎng)絡(luò)組合模型進行對比,對實際歷史風(fēng)速數(shù)據(jù)進行仿真預(yù)測。經(jīng)驗證,利用改進Elman神經(jīng)網(wǎng)絡(luò)修正ARIMA模型預(yù)測誤差,比單一ARIMA模型能夠更好的減小預(yù)測滯后性,提高預(yù)測精度、減小預(yù)測誤差；比ARIMA-BP神經(jīng)網(wǎng)絡(luò)組合模型訓(xùn)練速度提高了30%以上。 通過以上對風(fēng)速預(yù)測問題的研究,運用組合模型進行了較為深入的探討,并進行了數(shù)據(jù)處理及仿真,可以發(fā)現(xiàn)ARIMA-Elman神經(jīng)網(wǎng)絡(luò)組合模型比單一模型有更大的優(yōu)越性,為解決實際工程問題提供了一定的參考。
[Abstract]:Wind energy has attracted more and more attention due to its renewable and pollution-free characteristics. However, due to the volatility and randomness of wind speed, the wind power output is very unstable. With the increasing proportion of wind power generation in power grid, wind power generation is becoming more and more important. It will inevitably cause some adverse effects on the safe and stable operation of power system. The accuracy of wind speed prediction can provide a very important reference in wind power grid connection and power dispatching. The influence of wind speed fluctuation on power grid is greatly eliminated. For these reasons, it is very meaningful to predict the wind speed of wind farm. According to the nonlinear characteristics of wind speed data, the improved Elman neural network is used to modify the prediction results of ARIMA model. Using the combined model of time series and neural network, the short-term wind speed prediction is studied. First, the ARIMA model is used to predict the wind speed, and the linear law information is included in the prediction results of time series. The nonlinear law is included in the prediction error, and then the prediction error of ARIMA model and the first order difference sequence of historical wind speed are taken as the input variables of the improved Elman neural network. The wind speed prediction error of ARIMA model is taken as the output variable. Finally, the prediction result of ARIMA model is superposed with the error prediction result of Elman neural network, and the final modified predicted wind speed is obtained. In order to prove the effectiveness of the method, the actual historical wind speed data are simulated and forecasted by comparing with the single ARIMA model ARIMA-BP neural network combination model. The improved Elman neural network is used to correct the prediction error of ARIMA model, which can reduce the prediction lag, improve the prediction accuracy and reduce the prediction error better than the single ARIMA model. The training speed of the combined model is more than 30% higher than that of the ARIMA-BP neural network. Through the above research on wind speed prediction, the combined model is used to conduct a more in-depth discussion, and the data processing and simulation are carried out. It can be found that the combined model of ARIMA-Elman neural network has more advantages than the single model, which provides a certain reference for solving practical engineering problems.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614

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