支持向量機模型改進及在短期邊際電價預測中的應用
發(fā)布時間：2018-08-02 13:40
【摘要】：支持向量機預測模型雖然已被證實為一類優(yōu)于神經網絡等其他智能算法模型的新興預測技術，但其本身依舊不夠完美，仍然存在尚需改進的地方。 本文選取了支持向量機家族中的最小二乘支持向量機預測模型進行研究，并發(fā)現在使用該模型進行訓練時，由于其對訓練樣本的各個輸入向量的懲罰度是一樣的，即在訓練過程中對所有輸入點都等同看待，因此，一旦在訓練樣本集中出現離群點時，該點就會對預測模型產生一定的不良影響。這樣一來，勢必會降低系統(tǒng)的魯棒性，引起過學習，降低了預測模型的推廣能力和預測效果。 本文關注了此項問題，，嘗試對原模型進行改進，從而使得改進后的預測模型能夠很好地克服上述理論缺陷，優(yōu)化預測過程與結果，達到管理科學理論創(chuàng)新的目的。 為了達到上述目的，作者在經過了大量的研究與參考后，運用模糊數學中有關隸屬度問題的相關理論和方法與原預測模型相結合，建立了一套新的預測模型。為了考察該模型是否能達到對原預測模型的改進效果，并使得預測結果更佳，文章選取了美國PJM電力市場在2012年內的一些有關日前交易的負荷與邊際電價數據進行預測。首先，運用新模型對多個測試日的邊際電價進行預測，通過Matlab軟件加以實現，并得出了多個測試日的相關預測結果。之后，又運用原模型在相同條件下對相同日期的邊際電價進行預測并得出預測結果。最后，通過將兩種模型在多個測試日的預測值與真實值之間的誤差進行比較，發(fā)現新模型的預測誤差明顯低于原模型。這一點證實了新模型確實起到了對原模型明顯的改進效果，所得出的預測結果對未來發(fā)電企業(yè)的管理者作進一步分析與決策更加具有參考意義。另外，改進后的新模型在算法實現方面較之改進前的原模型并沒有明顯增加計算的復雜度，這又從另一個方面證實了該改進方案的有效性。 綜上所述，本文通過改進而得到的新模型可以被廣泛推廣，而且范圍不僅局限在電價預測領域之內。這對于管理科學理論創(chuàng)新與應用等領域的研究都具有一定的貢獻與啟發(fā)意義。
[Abstract]:Support vector machine (SVM) prediction model has been proved to be a kind of new prediction technology which is superior to other intelligent algorithm models such as neural network, but it itself is still not perfect, and there are still some problems to be improved. In this paper, the prediction model of least squares support vector machine (LS-SVM) in the family of support vector machines (SVM) is selected, and it is found that the penalty degree of each input vector of the training sample is the same when the model is used for training. That is, all input points are treated equally in the training process, so once the outliers appear in the training sample set, the outliers will have a certain adverse effect on the prediction model. In this way, the robustness of the system will be reduced, and the overlearning will be caused, and the generalization ability and prediction effect of the prediction model will be reduced. This paper focuses on this problem and attempts to improve the original model, so that the improved prediction model can overcome the above theoretical defects, optimize the prediction process and results, and achieve the purpose of theoretical innovation in management science. In order to achieve the above purpose, after a lot of research and reference, the author combines the theory and method of membership degree in fuzzy mathematics with the original prediction model, and establishes a new prediction model. In order to investigate whether the model can improve the original forecasting model and make the forecast result better, this paper selects some data of pre-day trading load and marginal electricity price of PJM electricity market in the United States in 2012 to forecast. Firstly, the new model is used to predict the marginal electricity price of multiple test days, which is realized by Matlab software, and the related prediction results of multiple test days are obtained. After that, the original model is used to predict the marginal electricity price of the same date under the same conditions and the prediction results are obtained. Finally, it is found that the prediction error of the new model is obviously lower than that of the original model by comparing the errors between the predicted values and the real values of the two models on multiple test days. This proves that the new model does improve the original model obviously, and the predicted results are more useful for the future power generation enterprise managers to make further analysis and decision making. In addition, the algorithm implementation of the improved new model does not significantly increase the computational complexity compared with the original model, which proves the effectiveness of the improved scheme from another aspect. To sum up, the new model can be extended widely, and the scope is not only limited to the field of electricity price prediction. It has a certain contribution and enlightening significance to the research of management science theory innovation and application.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F224;F416.61
本文編號：2159612
[Abstract]:Support vector machine (SVM) prediction model has been proved to be a kind of new prediction technology which is superior to other intelligent algorithm models such as neural network, but it itself is still not perfect, and there are still some problems to be improved. In this paper, the prediction model of least squares support vector machine (LS-SVM) in the family of support vector machines (SVM) is selected, and it is found that the penalty degree of each input vector of the training sample is the same when the model is used for training. That is, all input points are treated equally in the training process, so once the outliers appear in the training sample set, the outliers will have a certain adverse effect on the prediction model. In this way, the robustness of the system will be reduced, and the overlearning will be caused, and the generalization ability and prediction effect of the prediction model will be reduced. This paper focuses on this problem and attempts to improve the original model, so that the improved prediction model can overcome the above theoretical defects, optimize the prediction process and results, and achieve the purpose of theoretical innovation in management science. In order to achieve the above purpose, after a lot of research and reference, the author combines the theory and method of membership degree in fuzzy mathematics with the original prediction model, and establishes a new prediction model. In order to investigate whether the model can improve the original forecasting model and make the forecast result better, this paper selects some data of pre-day trading load and marginal electricity price of PJM electricity market in the United States in 2012 to forecast. Firstly, the new model is used to predict the marginal electricity price of multiple test days, which is realized by Matlab software, and the related prediction results of multiple test days are obtained. After that, the original model is used to predict the marginal electricity price of the same date under the same conditions and the prediction results are obtained. Finally, it is found that the prediction error of the new model is obviously lower than that of the original model by comparing the errors between the predicted values and the real values of the two models on multiple test days. This proves that the new model does improve the original model obviously, and the predicted results are more useful for the future power generation enterprise managers to make further analysis and decision making. In addition, the algorithm implementation of the improved new model does not significantly increase the computational complexity compared with the original model, which proves the effectiveness of the improved scheme from another aspect. To sum up, the new model can be extended widely, and the scope is not only limited to the field of electricity price prediction. It has a certain contribution and enlightening significance to the research of management science theory innovation and application.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F224;F416.61
【參考文獻】
相關期刊論文 前10條
1 黃日星,康重慶,夏清;電力市場中的邊際電價預測[J];電力系統(tǒng)自動化;2000年22期
2 楊莉,邱家駒,江道灼;基于BP網絡的下一交易日無約束市場清算價格預測模型[J];電力系統(tǒng)自動化;2001年19期
3 張顯;王錫凡;;短期電價預測綜述[J];電力系統(tǒng)自動化;2006年03期
4 蘇娟;杜松懷;李才華;;基于多因素小波分析的神經網絡短期現貨電價預測方法[J];電力自動化設備;2007年11期
5 陳建華,周浩;基于小腦模型關節(jié)控制器神經網絡的短期電價預測[J];電網技術;2003年08期
6 吳宏曉,侯志儉;基于免疫支持向量機方法的電力系統(tǒng)短期負荷預測[J];電網技術;2004年23期
7 楊延西,劉丁;基于小波變換和最小二乘支持向量機的短期電力負荷預測[J];電網技術;2005年13期
8 程曉鑫;周渝慧;;基于灰色改進模型的電價預測[J];華北電力大學學報;2006年01期
9 蘇娟;杜松懷;;GM(1,2)短期現貨電價灰色預測模型[J];繼電器;2006年01期
10 陳思杰;周浩;;電力市場電價預測方法綜述[J];繼電器;2006年11期
相關博士學位論文 前1條
1 馮業(yè)偉;基于支持向量機和移動Agent技術的銀行風險早期預警系統(tǒng)研究[D];中國海洋大學;2011年
相關碩士學位論文 前1條
1 梁懷翔;支持向量機遙感圖像分類的研究[D];長安大學;2011年
本文編號：2159612
本文鏈接：http://sikaile.net/guanlilunwen/shengchanguanlilunwen/2159612.html
最近更新
教材專著
