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

Lu, Yan; Niu, Dongxiao; Qiu, Jinpeng; Liu, Weidong

doi:10.1155/2015/651878

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…The input of the forecasting techniques was determined on the basis of optimal feature subset selection by the DIR. In reference [45], the authors divided the substation project cost into two main types: Primary and secondary production cost and individual project costs associated with site, totaling more than 20 factors. In reference [46], authors selected more than 26 variables including the area and main transformer capacity as the influencing factors of substation cost.…”

Section: Feature Selectionmentioning

confidence: 99%

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

et al. 2019

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Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA–DIR–DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels.

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Section: Feature Selectionmentioning

confidence: 99%

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

et al. 2019

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“…The type of project/facility is one of the major factors affecting cost, including density and functionality. Lu et al (2015) broke down the construction cost of transmission projects into four types of expenses: construction cost, equipment purchase cost, installation cost and other costs. Cost of labor, materials, equipment, item quantity, contract duration and location, quarter of year the contract was let, annual bid volume, bid variance volume and changes in plan, standards and specification were identified as the factors affecting highway construction cost by Wilmot and Mei (2005).…”

Section: Factors Impacting Project Costsmentioning

confidence: 99%

Comparing US public university and US private sector project costs

Strong

Glick

Syhail

2019

JFM

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Purpose This study aims to focus on the factors influencing project cost at US public universities and compares them to similar projects in the US private sector. It also presents an analysis of the potential reasons for the difference or similarities in the two sectors. Design/methodology/approach This study utilized an exploratory, comparative case study methodology performed on a small sample of public university projects and two sources of private sector cost data. Findings The results infer that most of the US public projects have comparable costs to that of their US private sector counterparts. The cost data from the university projects were further examined to explore if there were any possible relationships between the types of delivery methods used, sustainability certifications achieved and two project performance indexes – cost and duration. Research limitations/implications A more thorough analysis with a larger dataset is required to make generalizable conclusions. However, the process used in this study does provide a good overview of how facility managers could organize their own cost comparison study to evaluate their project expenditures. Practical implications This research provides a starting point for future research into the topic of US public sector project costs when compared to US private sector counterparts and the impact of delivery system and sustainability on cost of US public sector projects. Originality/value Research on this topic is scant; as such, this paper provides a starting point for future research and offers insights into the potential impacts of project delivery method and choice of following a sustainability certification option.

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“…Wang took the comprehensive cost index of transmission and transformation as the basis of project investment decisionmaking and probed into the establishment of a model for project investment cost prediction by using the Markov chain [16]. Lu et al took full account of the characteristics of subitem cost and adopted different methods to forecast separately and then superimposed to get the total cost [17]. Wang et al established the EEMD-BP model and used BP algorithm to forecast the trend components, and the final prediction results were obtained by considering the prediction values of the trend components and the fluctuation intervals [18].…”

Section: Introductionmentioning

confidence: 99%

Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA‐LSSVM‐KDE

Zhao

Miao

Zhen-gang

et al. 2019

Mathematical Problems in Engineering

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In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.

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

Cited by 9 publications

References 8 publications

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

Comparing US public university and US private sector project costs

Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA‐LSSVM‐KDE

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