本文選題：城市電網(wǎng) + 電量��； 參考：《華南理工大學(xué)》2014年碩士論文

【摘要】：城市電網(wǎng)電量（供電量或售電量）預(yù)測(cè)是電力市場(chǎng)中的一項(xiàng)基本工作。建立可靠的預(yù)測(cè)方法，做好城市電網(wǎng)電量預(yù)測(cè)工作可以科學(xué)指導(dǎo)發(fā)電機(jī)的出力和變壓器的經(jīng)濟(jì)運(yùn)行以及電氣設(shè)備檢修的合理安排，也可以為供電企業(yè)的營(yíng)銷(xiāo)和線損管理提供決策支持，對(duì)指導(dǎo)電氣設(shè)備檢修、電網(wǎng)經(jīng)濟(jì)運(yùn)行和推動(dòng)電力市場(chǎng)的發(fā)展都具有十分重要的意義。 在電量預(yù)測(cè)過(guò)程中，對(duì)季節(jié)性電量數(shù)據(jù)的預(yù)測(cè)存在三個(gè)亟需解決的問(wèn)題。一、電量季節(jié)性數(shù)據(jù)具有波動(dòng)性和趨勢(shì)性兩重趨勢(shì)的非線性特征，單一預(yù)測(cè)模型難以準(zhǔn)確描述這種非線性變化過(guò)程；二、電量季節(jié)性數(shù)據(jù)，既有自身的變化規(guī)律，又受到內(nèi)部和外部多因素影響而呈現(xiàn)一定的不確定性；在數(shù)學(xué)建模過(guò)程中，如果不能同時(shí)引入相關(guān)變量來(lái)進(jìn)行建模，將導(dǎo)致預(yù)測(cè)模型不能正確反應(yīng)電量數(shù)據(jù)的真實(shí)變化過(guò)程，使預(yù)測(cè)結(jié)果的精度和可信度降低。三、在電量預(yù)測(cè)過(guò)程中，最優(yōu)擬合模型不一定就是最優(yōu)預(yù)測(cè)模型；以擬合精度選擇預(yù)測(cè)模型的模型選擇機(jī)制，如果舍棄其它擬合精度沒(méi)那么高的預(yù)測(cè)模型，可能會(huì)遺失某些預(yù)測(cè)信息，，從而得不到正確的預(yù)測(cè)結(jié)果。 針對(duì)上述三種問(wèn)題，本文先提出了兩類(lèi)建模方法，一類(lèi)為基于電量數(shù)據(jù)變化規(guī)律的單維度預(yù)測(cè)方法；一類(lèi)為基于行業(yè)用電及相關(guān)因素的多維度預(yù)測(cè)方法�；陔娏繑�(shù)據(jù)變化規(guī)律的單維度預(yù)測(cè)方法包含了4個(gè)預(yù)測(cè)模型，這4個(gè)預(yù)測(cè)模型利用電量自身的數(shù)據(jù)建模，用不同的方法從不同的角度反映了電量自身的變化規(guī)律；基于行業(yè)用電及相關(guān)因素的多維度預(yù)測(cè)方法包含2個(gè)預(yù)測(cè)模型，這2個(gè)預(yù)測(cè)模型在數(shù)學(xué)建模過(guò)程中引入了行業(yè)用電和經(jīng)濟(jì)維度，從電量?jī)?nèi)部（行業(yè)用電）和外部（經(jīng)濟(jì)因素）體現(xiàn)電量的變化特征，彌補(bǔ)了基于電量變化規(guī)律的單維度預(yù)測(cè)方法沒(méi)有從其它維度反映出其它相關(guān)數(shù)據(jù)對(duì)電量影響的不足。再提出基于不同維度建模的城市電量預(yù)測(cè)方法，該方法運(yùn)用方差—協(xié)方差優(yōu)選組合法對(duì)兩類(lèi)不同維度的預(yù)測(cè)模型的所有預(yù)測(cè)信息進(jìn)行最大化利用，實(shí)現(xiàn)預(yù)測(cè)結(jié)果的最優(yōu)組合，提高了預(yù)測(cè)結(jié)果的精確度和可信度，為電量預(yù)測(cè)提供一種新思路。 為了使得本文提出的方法更易使用和推廣，利用MATLAB的GUI軟件包開(kāi)發(fā)了一套基于上述方法的預(yù)測(cè)軟件。并利用該軟件對(duì)廣東省某城市電網(wǎng)進(jìn)行實(shí)例分析，實(shí)例計(jì)算結(jié)果表明優(yōu)選組合預(yù)測(cè)結(jié)果中既包含了體現(xiàn)供電量自身變化規(guī)律的結(jié)果，又包含了體現(xiàn)行業(yè)用電及經(jīng)濟(jì)因素對(duì)供電量影響的結(jié)果，預(yù)測(cè)精度大幅優(yōu)于各單一模型。這說(shuō)明，這種方法預(yù)測(cè)性能優(yōu)越，大大提高了預(yù)測(cè)精度；開(kāi)發(fā)的軟件具有很高的實(shí)用價(jià)值。
[Abstract]:It is a basic work in the electricity market to predict the quantity of electricity (electricity supply or electricity sale) in urban power network. The establishment of reliable forecasting method and the work of electricity quantity prediction in urban power grid can scientifically guide generator output, economic operation of transformers and reasonable arrangement of electrical equipment maintenance. It can also provide decision support for marketing and line loss management of power supply enterprises. It is of great significance to guide the maintenance of electrical equipment, economic operation of power grid and promote the development of power market. In the process of electric quantity prediction, there are three problems that need to be solved in the prediction of seasonal electricity quantity data. First, the seasonal data of electricity quantity have the nonlinear characteristics of volatility and trend, so it is difficult for a single forecasting model to accurately describe the nonlinear change process; second, the seasonal data of electricity quantity has its own changing law. In the process of mathematical modeling, if the relevant variables can not be introduced to model at the same time, the prediction model will not correctly reflect the real change process of electricity data. The accuracy and reliability of the prediction results are reduced. Thirdly, in the process of electric quantity prediction, the optimal fitting model is not necessarily the optimal prediction model, and if the model selection mechanism of the prediction model is selected with the fitting accuracy, if the other prediction models with less fitting accuracy are abandoned, Some prediction information may be lost and the correct prediction results will not be obtained. In order to solve the above three problems, two kinds of modeling methods are proposed in this paper, one is a single-dimensional prediction method based on the law of change of electricity quantity data, the other is a multi-dimensional prediction method based on industry electricity consumption and related factors. The single dimensional forecasting method based on the change law of electricity quantity data includes four prediction models, which use the data of electricity itself to model, and reflect the change law of electricity quantity from different angles by different methods. The multi-dimensional forecasting method based on industry electricity consumption and related factors includes two forecasting models, which introduce the industry power consumption and economic dimensions in the process of mathematical modeling. The internal (industry) and external (economic factors) of electricity quantity reflect the characteristics of electricity quantity change, which makes up for the deficiency of the single dimension prediction method based on the law of electricity quantity change, which does not reflect the influence of other related data on electricity quantity from other dimensions. Then a method of city electricity forecasting based on different dimension modeling is proposed. The method uses variance-covariance optimal combination method to maximize the utilization of all prediction information of two kinds of different dimension prediction models, and realizes the optimal combination of prediction results. The accuracy and reliability of the prediction results are improved, and a new way of thinking is provided for the electric quantity prediction. In order to make the proposed method easier to use and popularize, a prediction software based on the above method is developed by using the GUI software package of MATLAB. The software is used to analyze an urban power network in Guangdong Province. The result of example calculation shows that the forecasting result of optimal combination includes the result that reflects the law of the change of electricity supply itself. It also includes the results of reflecting the influence of industry electricity consumption and economic factors on power supply, and the prediction accuracy is much better than that of each single model. This shows that this method has superior prediction performance and greatly improves the prediction accuracy, and the software developed has a high practical value.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TM727.2

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