Abstract
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.
Highlights
Mengjie Zhang and Amir MosaviAt present, a new round of power system reform is deepening, and the major changes in the supervision mode and profit mode of power grid enterprises are constantly exerting pressure on power grid enterprises
Different from other similar studies, this paper applies the emerging intelligent algorithm to the field of substation project cost prediction, carries out a more comprehensive technical index mining of substation engineering, and proposes an intelligent prediction model of substation project cost based on BP neural network optimized by sparrow search, which provides new ideas for substation project cost prediction research
It can be known from the above primary election library of sensitive factors of substation engineering cost that the technical factors of substation project cost are composed of construction engineering cost, installation engineering cost, equipment purchase cost, and other costs
Summary
A new round of power system reform is deepening, and the major changes in the supervision mode and profit mode of power grid enterprises are constantly exerting pressure on power grid enterprises. Existing research on index mining of engineering cost technology is relatively rough; at the same time, some intelligent prediction methods have improved the prediction technology, some algorithms are still not suitable for dealing with nonlinear problems and fall into local optimum, which is not highly applicable to substation projects that need to deal with a large amount of input data, and there is an urgent need to find an intelligent optimization algorithm to improve the prediction accuracy. Different from other similar studies, this paper applies the emerging intelligent algorithm to the field of substation project cost prediction, carries out a more comprehensive technical index mining of substation engineering, and proposes an intelligent prediction model of substation project cost based on BP neural network optimized by sparrow search, which provides new ideas for substation project cost prediction research. Research on Influencing Factors Identification of Substation Project Cost Based on
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
