【摘要】：科學技術的迅速發(fā)展使得電力工程通過擴張規(guī)模來滿足社會需求,電纜線路工程作為電力線路工程的重要工程之一,需求量不斷擴大,隨之而來工程投資不斷在增加,給電纜線路工程投資方帶來大量問題,傳統的工程造價預算方法已無法滿足實際工程需求,有必要研究新的科學方法對工程造價進行估算。近年隨著智能算法的引進,工程造價評估方面出現了很多評估方法,研究較多方法有模糊數學、灰色關聯度、神經網絡、粒子群及支持想回歸等理論技術。通過分析電纜線路歷史工程的特點,得出是歷史工程數據呈小樣本特點,屬于小樣本數據的學習問題。支持向量回歸對小樣本學習具有理想效果,因此將采用支持向量回歸模型對電纜線路工程造價評估,而實際應用中支持向量回歸由于參數選擇盲目性,計算精度和泛化能力有所欠缺,無法滿足實際應用的要求,粒子群算法對參數調節(jié)具有理想的效果,將借助粒子群優(yōu)化支持向量回歸參數,同時通過權衡隨機權重改進粒子群,提高粒子群調節(jié)參數的精度,增加評估模型穩(wěn)定性和估算精度。結合電纜線路工程初步設計,分析電纜線路工程造價數據特點,建立評估指標體系;通過數據歸一化對歷史工程數據進行標準化,利用主成分分析法對評估指標進行特征提取,提高指標的合理性和有效性;結合支持向量回歸和改進的粒子群算法,建立電纜線路工程造價評估模型并進行實例分析;實例結果及分析表明該模型可以有效地估算電纜線路工程造價,能夠有效指導電纜線路工程新建工程造價評估。
[Abstract]:The rapid development of science and technology makes electric power engineering meet the needs of the society by expanding its scale. As one of the important projects of power line engineering, the demand of cable line project is expanding constantly, and the project investment is increasing. It has brought a lot of problems to the investors of cable line engineering, and the traditional method of engineering cost budget can no longer meet the actual engineering demand. It is necessary to study a new scientific method to estimate the project cost. In recent years, with the introduction of intelligent algorithms, there have been many evaluation methods in engineering cost assessment. Many methods have been studied, such as fuzzy mathematics, grey correlation degree, neural network, particle swarm optimization and support regression. By analyzing the characteristics of the historical engineering of cable lines, it is concluded that the historical engineering data is characterized by a small sample, which belongs to the learning problem of the small sample data. Support vector regression has ideal effect on small sample learning, so support vector regression model will be used to evaluate cable line project cost, but in practical application, support vector regression is blind because of parameter selection. The calculation accuracy and generalization ability are not enough to meet the requirements of practical application. Particle swarm optimization (PSO) has an ideal effect on parameter adjustment. Particle swarm optimization (PSO) is used to optimize the parameters of support vector regression and to improve PSO by weighing the random weights. The accuracy of particle swarm optimization parameters is improved, and the stability and estimation accuracy of the evaluation model are increased. Combining with the preliminary design of cable line engineering, this paper analyzes the characteristics of cable line engineering cost data, establishes the evaluation index system, standardizes the historical engineering data by data normalization, and extracts the evaluation index by principal component analysis. Combining support vector regression and improved particle swarm optimization (PSO), the cost evaluation model of cable line engineering is established and the example is analyzed. The example results and analysis show that the model can effectively estimate the cost of cable line construction and can effectively guide the evaluation of new construction cost of cable line project.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TM75

【參考文獻】

相關期刊論文 前10條

1 陳潔;侯凱;高曉彬;;輸變電工程造價合理性評價方法研究[J];南方電網技術;2016年08期

2 章昀s，

本文編號：2215203