【摘要】：電網(wǎng)工程的造價是一個多變量、高度非線性的問題。過去對于輸變電工程造價的預測主要靠在該領域用有多年實踐經(jīng)驗的技術人員的實際分析和操作。但是當工程情況復雜多變時,很難通過經(jīng)驗估計得到單項工程可靠的結果用以指導工程造價控制。在變電工程中的單位容量造價和輸電工程中的單位長度造價是兩個投資方和施工單位最為關切的主要技術經(jīng)濟指標,也是輸變電工程造價管理與控制的核心指標。因此,投資方和施工單位迫切需要一種理想的預測方法能夠利用已建工程的歷史造價資料,快速預測出新建電力工程的主要技術經(jīng)濟指標,以便合理制定建造方案,為電力工程建設爭取主動時間,提高項目資金投入的審查效率和項目的質(zhì)量,指導新電力建工程的造價。論文對輸變電工程造價數(shù)據(jù)預處理技術進行研究,結合工程造價歷史數(shù)據(jù)的具體特點,提出包括數(shù)據(jù)清洗、數(shù)據(jù)轉(zhuǎn)換和數(shù)據(jù)約簡等內(nèi)容的輸變電工程造價數(shù)據(jù)預處理方法,并且以電力輸電工程為案例進行仿真,驗證數(shù)據(jù)預處理方法的有效性。然后,針對輸變電工程造價數(shù)據(jù)數(shù)據(jù),提出一種易于操作、快速有效的輸變電工程造價預測模型,即MEA-BP造價預測模型。其中,人工神經(jīng)網(wǎng)絡算法在小樣本學習領域表現(xiàn)十分優(yōu)越,也適合于對工程數(shù)目有限、影響因素頗多的輸變電工程造價數(shù)數(shù)據(jù)進行學習。思維進化算法作為強大的參數(shù)優(yōu)化算法,在模型中對BP人工神經(jīng)網(wǎng)絡參數(shù)進行優(yōu)化。最后利用實例對其進行了仿真模擬。證明其預測模型在精度方面相比于傳統(tǒng)的造價計量方法有了較大的提升,以電力輸電和變電工程為案例的仿真表明,該系統(tǒng)可以穩(wěn)定有效地實現(xiàn)工程造價管理,為工程建設的順利實施提供技術支持。
[Abstract]:The cost of power grid engineering is a multivariable and highly nonlinear problem. In the past, the cost prediction of transmission and transformation projects mainly depended on the practical analysis and operation of technicians with many years of practical experience in this field. However, when the engineering situation is complex and changeable, it is difficult to obtain reliable results of single project by empirical estimation to guide the project cost control. The unit capacity cost and the unit length cost in the power transmission project are the main technical and economic indexes concerned by the two investors and the construction units, as well as the core indexes of the cost management and control of the transmission and transformation projects. Therefore, the investors and construction units urgently need an ideal forecasting method which can quickly predict the main technical and economic indexes of the newly built electric power projects by using the historical cost data of the existing construction projects, so as to reasonably formulate the construction plans. It can gain active time for electric power engineering construction, improve the efficiency of project capital investment and project quality, and guide the cost of new electric power construction project. In this paper, the preprocessing technology of transmission and transformation project cost data is studied, and the pretreatment method of transmission and transformation engineering cost data including data cleaning, data conversion and data reduction is put forward according to the specific characteristics of historical data of project cost. The simulation of power transmission project is carried out to verify the validity of the data preprocessing method. Then, based on the cost data of transmission and transformation project, a fast and effective cost prediction model of transmission and transformation project, namely MEA-BP cost prediction model, is proposed. Among them, the artificial neural network algorithm is very superior in the field of small sample learning, and it is also suitable for learning the cost data of transmission and transformation projects with limited number of projects and many influential factors. As a powerful parameter optimization algorithm, the mental evolution algorithm optimizes the parameters of BP artificial neural network in the model. Finally, an example is used to simulate it. Compared with the traditional cost measurement method, the prediction model is proved to be more accurate. The simulation results of electric power transmission and substation projects show that the system can realize project cost management stably and effectively. To provide technical support for the smooth implementation of engineering construction.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F426.61;TP183;TU723.3

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