Preprint Article Version 1 This version is not peer-reviewed

The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects

Version 1 : Received: 4 May 2018 / Approved: 8 May 2018 / Online: 8 May 2018 (05:01:45 CEST)

How to cite: Wang, H.; Niu, D.; Li, S.; Wang, F.; Liang, Y. The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects. Preprints 2018, 2018050120 (doi: 10.20944/preprints201805.0120.v1). Wang, H.; Niu, D.; Li, S.; Wang, F.; Liang, Y. The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects. Preprints 2018, 2018050120 (doi: 10.20944/preprints201805.0120.v1).

Abstract

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness and generality in cost forecasting of substation projects.

Subject Areas

cost prediction of substation projects; improved least square support vector machine; wolf pack algorithm; data inconsistency rate

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