本文選題：電力負(fù)荷預(yù)測(cè) + 支持向量機(jī)　； 參考：《北京交通大學(xué)》2014年碩士論文

【摘要】：摘要：電力需求預(yù)測(cè)是電力系統(tǒng)規(guī)劃的重要組成部分。本論文主要對(duì)電力需求中的月度和年度負(fù)荷進(jìn)行預(yù)測(cè)。它們的特點(diǎn)是歷史數(shù)據(jù)少,受經(jīng)濟(jì)、社會(huì)等不確定因素影響較大。準(zhǔn)確的負(fù)荷預(yù)測(cè)有利于提高電網(wǎng)運(yùn)行的安全穩(wěn)定性,有效地降低發(fā)電成本,保證用電需求,增強(qiáng)供電可靠性,從而提高電力系統(tǒng)的經(jīng)濟(jì)效益和社會(huì)效益。 本文介紹了電力系統(tǒng)負(fù)荷預(yù)測(cè)的目的和意義,對(duì)國內(nèi)外負(fù)荷預(yù)測(cè)的現(xiàn)狀進(jìn)行綜述。介紹了負(fù)荷預(yù)測(cè)的基本原理,分析了各種方法的優(yōu)缺點(diǎn)。闡述了電力負(fù)荷的分類及特點(diǎn),給出了電力負(fù)荷預(yù)測(cè)的模型要求和誤差指標(biāo)。同時(shí)對(duì)支持向量機(jī)的文獻(xiàn)進(jìn)行綜述,指出支持向量機(jī)的優(yōu)點(diǎn)和存在的問題。并且對(duì)影響電力需求的因素做了分析。 本文在分析了電力負(fù)荷預(yù)測(cè)特點(diǎn)的基礎(chǔ)上,采用支持向量回歸機(jī)算法對(duì)山西電網(wǎng)月度最大電力負(fù)荷進(jìn)行預(yù)測(cè)。介紹了支持向量機(jī)算法的基本原理,建立基于該方法的負(fù)荷預(yù)測(cè)模型,給出基本算法流程圖,利用MATLAB進(jìn)行程序設(shè)計(jì),實(shí)現(xiàn)上述算法過程。通過預(yù)測(cè)結(jié)果分析并與其他方法進(jìn)行比較,驗(yàn)證了該智能預(yù)測(cè)方法的可行性。 然后在分析了支持向量回歸機(jī)的各參數(shù)對(duì)其性能有很大影響的基礎(chǔ)上,結(jié)合月度最大電力負(fù)荷的特點(diǎn),提出了利用粒子群優(yōu)化支持向量負(fù)荷預(yù)測(cè)模型,并通過權(quán)衡近期數(shù)據(jù)法整理月度負(fù)荷數(shù)據(jù)。同時(shí)給出了粒子群優(yōu)化支持向量機(jī)模型的原理及流程圖。通過實(shí)際算例分析,與標(biāo)準(zhǔn)支持向量回歸機(jī)方法的預(yù)測(cè)結(jié)果進(jìn)行比較,驗(yàn)證粒子群優(yōu)化后的支持向量回歸機(jī)負(fù)荷預(yù)測(cè)模型具有預(yù)測(cè)精度高、計(jì)算量小等優(yōu)勢(shì)。 同時(shí)提出了基于粒子群優(yōu)化支持向量機(jī)負(fù)荷預(yù)測(cè)模型的改進(jìn)措施,增加了慣性權(quán)重因子,并提出了三點(diǎn)平滑法優(yōu)化數(shù)據(jù),使模型更加完善。給出改進(jìn)后的算法流程圖,通過預(yù)測(cè)山西月度負(fù)荷,驗(yàn)證了改進(jìn)型的粒子群優(yōu)化支持向量機(jī)負(fù)荷預(yù)測(cè)模型的預(yù)測(cè)精度更高。在前文分析了電力需求影響因素的基礎(chǔ)上,針對(duì)年度負(fù)荷的特點(diǎn),整理輸入數(shù)據(jù),利用建立好的模型預(yù)測(cè)未來年份的最大電力負(fù)荷。 最后,介紹國內(nèi)外常用的基于支持向量機(jī)的算法程序。分析了網(wǎng)格搜索法優(yōu)化支持向量回歸機(jī)的特點(diǎn),運(yùn)用基于網(wǎng)格搜索法優(yōu)化支持向量機(jī)的CMSVM軟件對(duì)山西省電力負(fù)荷進(jìn)行預(yù)測(cè)研究。并對(duì)基于支持向量機(jī)的負(fù)荷預(yù)測(cè)所需要注意的關(guān)鍵問題做出總結(jié),并提出建議。
[Abstract]:Abstract: power demand forecasting is an important part of power system planning. This paper mainly forecasts the monthly and annual load in power demand. They are characterized by the lack of historical data, economic, social and other uncertain factors. Accurate load forecasting can improve the safety and stability of power system, reduce the cost of generation, ensure the demand of electricity, enhance the reliability of power supply, and improve the economic and social benefits of power system. This paper introduces the purpose and significance of power system load forecasting, and summarizes the current situation of load forecasting at home and abroad. The basic principle of load forecasting is introduced, and the advantages and disadvantages of various methods are analyzed. This paper expounds the classification and characteristics of power load, and gives the model requirements and error index of power load forecasting. At the same time, the paper summarizes the literature of support vector machine, and points out the advantages and problems of support vector machine. The factors that affect the power demand are also analyzed. Based on the analysis of the characteristics of power load forecasting, the support vector regression algorithm is used to forecast the maximum monthly power load of Shanxi power network. The basic principle of support vector machine (SVM) algorithm is introduced, the load forecasting model based on this method is established, the flow chart of basic algorithm is given, and the program is designed by using MATLAB to realize the above algorithm process. The feasibility of the intelligent prediction method is verified by analyzing the prediction results and comparing with other methods. Then, based on the analysis of the influence of the parameters of support vector regression machine on its performance, combined with the characteristics of the maximum monthly power load, a support vector load forecasting model based on particle swarm optimization is proposed. And collate monthly load data by tradeoff short-term data method. At the same time, the principle and flow chart of particle swarm optimization support vector machine model are given. Compared with the prediction results of the standard support vector regression method, it is verified that the load forecasting model based on particle swarm optimization has the advantages of high forecasting accuracy and less calculation. At the same time, an improved support vector machine load forecasting model based on particle swarm optimization is proposed. The inertia weight factor is added, and the three-point smoothing method is proposed to optimize the data to make the model more perfect. The flow chart of the improved algorithm is given. By forecasting the monthly load in Shanxi, it is verified that the improved particle swarm optimization support vector machine forecasting model has higher forecasting accuracy. Based on the analysis of the influencing factors of power demand, the input data are arranged according to the characteristics of the annual load, and the established model is used to predict the maximum power load in the future year. At last, the algorithm program based on support vector machine is introduced. The characteristics of optimized support vector regression (SVM) based on grid search method are analyzed, and the power load forecasting of Shanxi province is studied by using CMSVM software based on grid search optimization support vector machine (SVM). The key problems of load forecasting based on support vector machine are summarized and some suggestions are put forward.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM715

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本文編號(hào)：1963537