Abstract
Due to the many factors affecting the cost of transmission line engineering and the lack of mutual independence, it is difficult to predict the cost. Firstly, the principal component analysis is used to process the original indicator data, eliminating the correlation between the original indicators and extracting the potential comprehensive independent indicators. Then, the new indicator is used as the input set to construct the predictive learning model based on the least squares support vector machine, and the predicted output and the actual value are compared and analyzed. The results show that the model can achieve the desired prediction effect in the case of small samples.
Highlights
The cost level of transmission line engineering is a multivariable and highly nonlinear problem, which is manifested in the fact that there are many factors affecting the cost of transmission and transformation engineering, and they are not independent of each other, but form a complex relationship
It has made it more difficult to predict the cost of power transmission and transformation projects
With the application of some mathematical methods and intelligent algorithms in forecasting, many new prediction methods have emerged, including classical prediction methods represented by trend analysis and regression analysis, and intelligent prediction methods represented by neural networks and support vector machines[2]
Summary
The cost level of transmission line engineering is a multivariable and highly nonlinear problem, which is manifested in the fact that there are many factors affecting the cost of transmission and transformation engineering, and they are not independent of each other, but form a complex relationship. Used cost prediction methods include fuzzy mathematics, analog engineering, etc., but these methods are cumbersome and the prediction accuracy can not meet the demand of reasonable cost prediction[1]. With the application of some mathematical methods and intelligent algorithms in forecasting, many new prediction methods have emerged, including classical prediction methods represented by trend analysis and regression analysis, and intelligent prediction methods represented by neural networks and support vector machines[2]. The intelligent prediction method has great advantages in dealing with nonlinear problems, and the prediction accuracy is greatly improved compared with the classical prediction method, but it is prone to over-fitting, falling into local optimum and so on[3]. Principal component analysis is firstly adopted for factor dimensionality reduction, and the least square support vector machine is used for cost prediction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
