本文選題：電網(wǎng) + 負(fù)荷預(yù)測　； 參考：《華北電力大學(xué)》2016年碩士論文

【摘要】：實(shí)際生活中有很多因素,例如政治、經(jīng)濟(jì)、重大事故等,會(huì)不同程度的影響電力系統(tǒng)中的電網(wǎng)中電壓、電流等的穩(wěn)定運(yùn)行,這樣會(huì)造成電網(wǎng)負(fù)荷的波動(dòng)性,進(jìn)而造成電網(wǎng)系統(tǒng)和電氣設(shè)備的非正常運(yùn)行以及其它損害等。因此,對電力系統(tǒng)中電網(wǎng)負(fù)荷的事先有效估計(jì),是對電力系統(tǒng)進(jìn)行合理經(jīng)濟(jì)調(diào)度、降低生產(chǎn)成本、防止電網(wǎng)大面積停電或者電網(wǎng)崩潰的迫切需求。電力系統(tǒng)中負(fù)荷預(yù)測是電力系統(tǒng)穩(wěn)定運(yùn)行的基礎(chǔ),是通過歷史負(fù)荷的探究和分析,應(yīng)用特定的分析方法來預(yù)測未來負(fù)荷。本文主要研究電力系統(tǒng)中電網(wǎng)負(fù)荷的較短期預(yù)測,并且針對如何建立預(yù)測精度更高和計(jì)算速度更快的預(yù)測模型進(jìn)行了分析探索。本文所做的工作如下:1)本文基于電網(wǎng)中歷史的負(fù)荷數(shù)據(jù)規(guī)律分析,采用基礎(chǔ)灰色模型,即GM(1,1)灰色模型來預(yù)測地方電網(wǎng)中的負(fù)荷數(shù)值,在建模過程中提出動(dòng)態(tài)新息模型建模。2)對預(yù)測結(jié)果精度檢驗(yàn)的方法本文采用三種方法:相對誤差檢驗(yàn)法、后驗(yàn)差檢驗(yàn)法和關(guān)聯(lián)度檢驗(yàn)法。3)為了提高GM(1,1)灰色模型對電網(wǎng)負(fù)荷的預(yù)測精度,本文提出三種方法來提高模型預(yù)測精度,第一種方法主要是改善優(yōu)化GM(1,1)建模過程中的自身模型,即GM(1,1)灰色模型背景值的優(yōu)化、GM(1,1)灰色模型灰色導(dǎo)數(shù)的優(yōu)化、GM(1,1)灰色模型初始條件的合理選擇等;第二種方法主要是應(yīng)用融合GM(1,1)灰色模型的組合模型預(yù)測方法,將灰色預(yù)測模型分別與最小二乘法、指數(shù)平滑法、人工神經(jīng)網(wǎng)路組合優(yōu)化來預(yù)測電網(wǎng)負(fù)荷數(shù)據(jù),通過彌補(bǔ)單一使用模型的不足的方式提高預(yù)測精度,期望達(dá)到良好的預(yù)測效果;第三種方法是基于RBF神經(jīng)網(wǎng)絡(luò)對改進(jìn)GM(1,1)殘差修正的負(fù)荷預(yù)測模型來對電網(wǎng)負(fù)荷進(jìn)行分析和預(yù)測。通過最終對模型預(yù)測電網(wǎng)負(fù)荷的結(jié)果對比分析比較,可得出本文提出的三種提高模型預(yù)測精度的方法切實(shí)可靠,取得了很好的預(yù)測效果。
[Abstract]:There are many factors in real life, such as politics, economy, major accidents, which will affect the steady operation of voltage and current in power system to some extent, which will result in the fluctuation of power grid load. It also causes abnormal operation and other damage of power system and electrical equipment. Therefore, it is an urgent need to estimate the load of power system in advance, which is necessary for reasonable economic dispatching, reducing production cost and preventing large area power outages or power network collapse. Load forecasting in power system is the basis of stable operation of power system. Through the exploration and analysis of historical load, a specific analysis method is applied to forecast future load. This paper mainly studies the short-term forecasting of power network load, and analyzes and explores how to establish a forecasting model with higher forecasting accuracy and faster calculation speed. The work of this paper is as follows: (1) based on the analysis of the law of historical load data in the power network, this paper uses the basic grey model, that is, GM1 / 1) grey model, to predict the load value in the local power network. In the course of modeling, the dynamic innovation model modeling. 2) the method of testing the accuracy of prediction results is presented in this paper: the relative error test method, the relative error test method, the relative error test method, the relative error test method, In order to improve the forecasting accuracy of GM1 / 1) grey model, this paper presents three methods to improve the forecasting accuracy of the model. The first method is mainly to improve the self-model in the process of optimizing GM-1). That is, the optimization of the background value of the grey model, the optimization of the grey derivative of the grey model and the reasonable selection of the initial conditions of the grey model, the second method is mainly the application of the combined model prediction method of the combined grey model. The grey forecasting model is combined with the least square method, exponential smoothing method and artificial neural network to forecast the load data of the power network. The forecasting accuracy is improved by making up for the shortage of the single model, and the prediction effect is expected to be good. The third method is based on the modified RBF neural network (RBF) residual error correction load forecasting model to analyze and forecast the power grid load. Through the comparison and comparison of the results of the model forecasting power network load, it is concluded that the three methods proposed in this paper to improve the forecasting accuracy of the model are practical and reliable, and good prediction results have been obtained.
【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】：2016
【分類號】：TM715

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