Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 651878, 11 pages

http://dx.doi.org/10.1155/2015/651878

## Prediction Technology of Power Transmission and Transformation Project Cost Based on the Decomposition-Integration

^{1}North China Electric Power University, No. 2, Beinong Road, Huilongguan, Changping District, Beijing 102206, China^{2}Zhejiang Electric Power Corporation, Economic Institute of Technology, No. 1, Nanfu Road, Shangcheng District, Zhejiang 310008, China^{3}Xi’an Jiaotong University, No. 74, West Road, Yanta District, Xi’an 710049, China

Received 29 October 2014; Revised 10 March 2015; Accepted 17 March 2015

Academic Editor: Alex Elías-Zúñiga

Copyright © 2015 Yan Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Through the analysis of power transmission and transformation project cost, total cost can be decomposed into construction cost, equipment purchase cost, installation cost, and other costs. This paper proposes a decomposition-integration cost prediction model taking a substation project as an example by fully considering the cost characteristics. In decomposition module, the total cost is decomposed into four expenses. In prediction module, different forecasting models are selected to forecast different expense. In integrated module, choose different integration methods to get the predicting results of total cost. The empirical results show that decomposition-integration prediction algorithm has good effect which can effectively predict the cost of power transmission and transformation project and has practical application and popularization value.

#### 1. Introduction

As the infrastructure construction of national economy, good construction and stable operation of power transmission and transformation project not only directly affect the development of the electric power industry, but also have a huge ripple effect on other related industries. With the high-speed development of national economy and rapid growth in electricity demand, electric power construction has accelerated [1]. However, the electric power construction investment is limited. If the project cost control is undeserved, rising cost level will affect the power grid project economic benefits and the level of construction. So how can we determine and control the power transmission and transformation project cost is the main problem to be solved in the power engineering cost management. At present, there are a lot of related researches on electric power engineering cost prediction. The common cost prediction models are multiple regression, nonlinear regression, linear network prediction, BP network prediction, fuzzy neural network, and so forth. The literature [2] used the gray GM (1,1) method to establish two calculation models, which were used to the estimation budget establishment of the power engineering. The literature [3] established the Markov chain model to accurately forecast the power transmission project cost index. The literature [4, 5] elaborated the key influence factors of the power transmission and transformation project cost, using multiple linear regression method to establish comprehensive cost prediction model. The literature [6, 7] proposed a kind of transmission line engineering cost based on the BP neural network prediction method. The literature [8] estimated the project cost by calculating the similarity degree between the completed projects and projects based on fuzzy mathematics theory. The literature [9, 10] constructed the engineering cost estimation model of support vector machine. Any single forecast model has its own advantages and disadvantages. In order to improve the accuracy of cost forecast, some mixed models appeared in the field of power engineering cost prediction. The literature [11] proposed a cost estimation method based on gray relational analysis and neural network. The literature [12] established project cost prediction model based on chaos SVM and ARIMA models.

As a whole, domestic and foreign engineering cost prediction methods can be summarized as the following kinds: first one is the method of quota with disadvantages of too long file budgeting time and complex budget work. The second is the engineering analogy method with poor accuracy. The third is the fuzzy mathematics method which estimates the project cost by the similar engineering fuzzy number conversion, while the determination of feature membership and coefficient adjustment is too difficult. The fourth is regression analysis; the disadvantage is that it cannot take enough factors, especially the uncertainty factors, into consideration [13]. The fifth is artificial neural network algorithm which is intelligent and has a strong ability to find the rules, but the training process may face problems of no convergence, being easy to fall into local minimum point or unable to get global optimization, and so forth. The sixth is combination forecast method; at present, the research on the applying of this model in electric power engineering cost is rare.

Based on the above literature analysis, combined with the idea of “decomposition-integration” and the perspective of combination cost forecasting, this paper builds a decomposition-integration power project cost forecasting model to overcome the shortcomings of the various cost prediction models. As for the decomposition module, the power transmission and transformation project cost is decomposed into itemized expenses which can be simply described. As for the prediction module, the main influence factors are identified and the suitable forecasting model is selected according to the characteristics of the itemized expenses. As for the integration module, choose effective integration algorithm to restore prediction system. At last, this paper chooses the cost data of Zhejiang province power transmission and transformation project for the example simulation. The results show that it can predict the power project cost effectively using the decomposition-integration method.

#### 2. Decomposition-Integration Prediction Technology Process

Decomposition-integration of power transmission and transformation project cost prediction technology can be divided into three modules: decomposition module, prediction module, and integration module. And the prediction technology is discussed based on the example of a substation project.

*Decomposition Module.* According to “Grid Construction Budgeting and Calculating Standard,” the static investment of new substation engineering includes primary and secondary production engineering costs, individual project costs associated with site, compiled years spreads, and other expenses. The main production engineering costs are divided into construction project cost, installation cost, and equipment purchase expense according to the cost types. Therefore, in terms of cost types, substation project costs can be decomposed into construction project cost, installation cost, equipment purchase cost, and other costs [14].

*Prediction Module.* Cost prediction method is numerous, including multiple linear regression, artificial neural network, and support vector machine. On the basis of cost decomposition, identify, analyze, and filter its influencing factors; then, select the suitable method for each of itemized expenses from the prediction method library to itemized forecast.

*Integration Modules.* Applying two methods to integrate costs, the first is simple summation, namely, to add the construction costs, installation costs, equipment purchase costs, and other expenses to get the final prediction; the other is the weighted summation, namely, to work out the weight of costs through analyzing the proportion of the four cost items and then calculate the weighted summation.

*Feedback Regulation.* Compare the prediction results to the actual value with mean absolute error (MAE) and mean square error (MSE), and constantly optimize the analysis and prediction methods according to the feedback adjustment of fitting effect. The training and optimization can be stopped only when the forecast cost data achieve the prediction accuracy. Decomposition-integrated prediction process is shown in Figure 1.