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

【摘要】：城市電網電量（供電量或售電量）預測是電力市場中的一項基本工作。建立可靠的預測方法，做好城市電網電量預測工作可以科學指導發(fā)電機的出力和變壓器的經濟運行以及電氣設備檢修的合理安排，也可以為供電企業(yè)的營銷和線損管理提供決策支持，對指導電氣設備檢修、電網經濟運行和推動電力市場的發(fā)展都具有十分重要的意義。 在電量預測過程中，對季節(jié)性電量數據的預測存在三個亟需解決的問題。一、電量季節(jié)性數據具有波動性和趨勢性兩重趨勢的非線性特征，單一預測模型難以準確描述這種非線性變化過程；二、電量季節(jié)性數據，既有自身的變化規(guī)律，又受到內部和外部多因素影響而呈現一定的不確定性；在數學建模過程中，如果不能同時引入相關變量來進行建模，將導致預測模型不能正確反應電量數據的真實變化過程，使預測結果的精度和可信度降低。三、在電量預測過程中，最優(yōu)擬合模型不一定就是最優(yōu)預測模型；以擬合精度選擇預測模型的模型選擇機制，如果舍棄其它擬合精度沒那么高的預測模型，可能會遺失某些預測信息，，從而得不到正確的預測結果。 針對上述三種問題，本文先提出了兩類建模方法，一類為基于電量數據變化規(guī)律的單維度預測方法；一類為基于行業(yè)用電及相關因素的多維度預測方法。基于電量數據變化規(guī)律的單維度預測方法包含了4個預測模型，這4個預測模型利用電量自身的數據建模，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)用電及相關因素的多維度預測方法包含2個預測模型，這2個預測模型在數學建模過程中引入了行業(yè)用電和經濟維度，從電量內部（行業(yè)用電）和外部（經濟因素）體現電量的變化特征，彌補了基于電量變化規(guī)律的單維度預測方法沒有從其它維度反映出其它相關數據對電量影響的不足。再提出基于不同維度建模的城市電量預測方法，該方法運用方差—協(xié)方差優(yōu)選組合法對兩類不同維度的預測模型的所有預測信息進行最大化利用，實現預測結果的最優(yōu)組合，提高了預測結果的精確度和可信度，為電量預測提供一種新思路。 為了使得本文提出的方法更易使用和推廣，利用MATLAB的GUI軟件包開發(fā)了一套基于上述方法的預測軟件。并利用該軟件對廣東省某城市電網進行實例分析，實例計算結果表明優(yōu)選組合預測結果中既包含了體現供電量自身變化規(guī)律的結果，又包含了體現行業(yè)用電及經濟因素對供電量影響的結果，預測精度大幅優(yōu)于各單一模型。這說明，這種方法預測性能優(yōu)越，大大提高了預測精度；開發(fā)的軟件具有很高的實用價值。
[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.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM727.2

【參考文獻】

相關期刊論文 前4條

1 康重慶,夏清,沈瑜,相年德;電力系統(tǒng)負荷預測的綜合模型[J];清華大學學報(自然科學版);1999年01期

2 牛東曉,陳志業(yè),邢棉,謝宏;具有二重趨勢性的季節(jié)型電力負荷預測組合優(yōu)化灰色神經網絡模型[J];中國電機工程學報;2002年01期

3 謝敬東,唐國慶,徐高飛,陳高文;組合預測方法在電力負荷預測中的應用[J];中國電力;1998年06期

4 周淼;;湖南省人均需電量回歸模型的建立及其預測應用[J];中南林業(yè)科技大學學報(社會科學版);2010年04期

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