Cost Forecast of Intelligent Substation Project Based on Index Evaluation and Artificial Bee Colony Optimized Support Vector Regression

Autor:	Zhengsen Ji, Shiping Geng, Dongxiao Niu, Zhuxiao Tian
Rok vydání:	2021
Předmět:	Support vector machine Electric power system Index (economics) Computer science Dimensionality reduction Control (management) Sample (statistics) Grey relational analysis Random forest Reliability engineering
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030700416 CSIA (1)
DOI:	10.1007/978-3-030-70042-3_107
Popis:	With the complexity of the power system, effective control of the cost level of substation engineering projects is of great significance to improve investment efficiency. However, the index screening model in the current cost forecasting model is relatively single and one-sided, which leads to many important factors missing. This paper proposes an index evaluation model based on random forest (RF) and grey relational analysis (GRA), comprehensively evaluates the indexes to achieve reasonable dimension reduction, and then uses the artificial bee colony optimized support vector regression (ABC-SVR) model to predict the static total investment of the substation project. Taking the substation projects in Jiangsu Province in the past three years as a sample, the results show that the model proposed in this paper not only retains important indicators, but also achieves high prediction accuracy, which is of great significance for practical applications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::722f531d063e5dd61c9eb79f2fd7c4e5 https://doi.org/10.1007/978-3-030-70042-3_107 Zobrazit plný text záznamu