【摘要】：科學(xué)技術(shù)的迅速發(fā)展使得電力工程通過擴(kuò)張規(guī)模來(lái)滿足社會(huì)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(kuò)大,隨之而來(lái)工程投資不斷在增加,給電纜線路工程投資方帶來(lái)大量問題,傳統(tǒng)的工程造價(jià)預(yù)算方法已無(wú)法滿足實(shí)際工程需求,有必要研究新的科學(xué)方法對(duì)工程造價(jià)進(jìn)行估算。近年隨著智能算法的引進(jìn),工程造價(jià)評(píng)估方面出現(xiàn)了很多評(píng)估方法,研究較多方法有模糊數(shù)學(xué)、灰色關(guān)聯(lián)度、神經(jīng)網(wǎng)絡(luò)、粒子群及支持想回歸等理論技術(shù)。通過分析電纜線路歷史工程的特點(diǎn),得出是歷史工程數(shù)據(jù)呈小樣本特點(diǎn),屬于小樣本數(shù)據(jù)的學(xué)習(xí)問題。支持向量回歸對(duì)小樣本學(xué)習(xí)具有理想效果,因此將采用支持向量回歸模型對(duì)電纜線路工程造價(jià)評(píng)估,而實(shí)際應(yīng)用中支持向量回歸由于參數(shù)選擇盲目性,計(jì)算精度和泛化能力有所欠缺,無(wú)法滿足實(shí)際應(yīng)用的要求,粒子群算法對(duì)參數(shù)調(diào)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(shù),同時(shí)通過權(quán)衡隨機(jī)權(quán)重改進(jìn)粒子群,提高粒子群調(diào)節(jié)參數(shù)的精度,增加評(píng)估模型穩(wěn)定性和估算精度。結(jié)合電纜線路工程初步設(shè)計(jì),分析電纜線路工程造價(jià)數(shù)據(jù)特點(diǎn),建立評(píng)估指標(biāo)體系;通過數(shù)據(jù)歸一化對(duì)歷史工程數(shù)據(jù)進(jìn)行標(biāo)準(zhǔn)化,利用主成分分析法對(duì)評(píng)估指標(biāo)進(jìn)行特征提取,提高指標(biāo)的合理性和有效性;結(jié)合支持向量回歸和改進(jìn)的粒子群算法,建立電纜線路工程造價(jià)評(píng)估模型并進(jìn)行實(shí)例分析;實(shí)例結(jié)果及分析表明該模型可以有效地估算電纜線路工程造價(jià),能夠有效指導(dǎo)電纜線路工程新建工程造價(jià)評(píng)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(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TM75

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本文編號(hào)：2215203