Intelligent Prediction of Power Grid Project Cost Based on PSO-BP Model

2021 ◽  
Author(s):  
Dan Chen ◽  
Daojun Ding ◽  
Xiaomeng Zhai ◽  
Xiang Zhou ◽  
Huichuan Liu ◽  
...  
Keyword(s):  
Power Grid ◽  
Project Cost ◽  
Bp Model

scholarly journals Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

2021 ◽  
Vol 13 (24) ◽  
pp. 13746
Author(s):  
Xiaomin Xu ◽  
Luyao Peng ◽  
Zhengsen Ji ◽  
Shipeng Zheng ◽  
Zhuxiao Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Search Algorithm ◽  
Power Grid ◽  
Engineering System ◽  
Project Cost ◽  
Total Investment ◽  
Engineering Cost

The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

scholarly journals Power Grid Project Cost Intelligent Evaluation Technology Based on Probability Statistics

2014 ◽  
Vol 8 (1) ◽  
pp. 1129-1133
Author(s):  
Yan Lu ◽  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Mian Xing
Keyword(s):  
Power Grid ◽  
Project Cost

scholarly journals Automatic analysis model of power grid infrastructure project cost level

2021 ◽  
Vol 1748 ◽  
pp. 052041
Author(s):  
Yubin Lin ◽  
Zhenda Hu ◽  
Chengwei Zhang ◽  
Benzhao Fu ◽  
Shiming Zhang ◽  
...  
Keyword(s):  
Power Grid ◽  
Automatic Analysis ◽  
Analysis Model ◽  
Grid Infrastructure ◽  
Project Cost ◽  
Infrastructure Project

scholarly journals Research on the Integrated Application of 3D Design and Project Cost based on Digital Power Grid

2021 ◽  
Vol 632 ◽  
pp. 042073
Author(s):  
Hengwu Zhang ◽  
Kehui Yan ◽  
Xiaofeng Ma ◽  
Jinming Li ◽  
Peng Fang
Keyword(s):  
Power Grid ◽  
3D Design ◽  
Project Cost

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

2020 ◽  
Vol 213 ◽  
pp. 03029
Author(s):  
Zuobin Liang ◽  
Shan Gao ◽  
Qing Wang ◽  
Yong Dai ◽  
Peng Rong ◽  
...  
Keyword(s):  
Investment Decision ◽  
Power Grid ◽  
Analysis Model ◽  
Project Cost ◽  
Management Level ◽  
Whole Process ◽  
Management Objectives ◽  
Investment Decision Making ◽  
Investment Process ◽  
Control And Management

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.

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