Research on the Construction Method of Intelligent Prediction and Analysis Model for the Whole Process of Power Grid Project Cost

Zuobin Liang,Peng Rong,Qing Wang,Yunxing Gao,Shan Gao,Yong Dai,Jun Ye,X Wei

doi:10.1051/e3sconf/202021303029

Abstract

The government’s supervision of power grid enterprises will gradually focus on the transmission and distribution price, and the investment and income will be more strictly supervised. Under the new management requirements, the company must pay more attention to the compliance of the investment process, further strengthen the investment risk control, put an end to inefficient or invalid investment, strengthen the all-round and whole process supervision, and scientifically and accurately determine and carry out effective project cost control and management. It is the key to achieve project management objectives, and also an important measure of investment fine management and control. This paper takes historical cost data as the research object, constructs the whole process intelligent prediction and analysis model of power grid project cost, assists investment decision-making, reduces the balance rate, and improves the efficiency and efficiency of the company’s investment and lean management level.

Highlights

Research backgroundThe investment projects of power transmission and transformation projects are increasing year by year, and the scale of power construction is expanding
Based on completed final accounts of power grid infrastructure projects of 35 kV and above, and individual real-time cost data, build a real-time basic cost database, use statistical analysis methods and artificial intelligence algorithms to build cost prediction models, and use combination weighting on the prediction results to achieve project cost Prediction
3.1.Construction of real-time cost basic database dynamic investment, the logic is unreasonable and it is judged as a suspected problem sample

Summary

Research background

The investment projects of power transmission and transformation projects are increasing year by year, and the scale of power construction is expanding. Based on completed final accounts of power grid infrastructure projects of 35 kV and above, and individual real-time cost data, build a real-time basic cost database, use statistical analysis methods and artificial intelligence algorithms to build cost prediction models, and use combination weighting on the prediction results to achieve project cost Prediction. If the absolute value of the single project dynamic investment balance rate exceeds 30%, it is judged as a suspected problem sample. Based on the general design or general cost typical scheme library, this paper uses statistical analysis method to calculate the average cost, cost interval and unit scale cost level of various types of projects, and establishes the standard base of unit cost investment reference value. The construction idea of artificial intelligence project cost prediction model based on neural network is shown in the following figure: Engineering variables.

Intelligent cost analysis and evaluation model

Conclusion