基于改进萤火虫算法优化SVM的变电工程造价预测

doi:10.11930/j.issn.1004-9649.2017.03.168.06

中国电力 ›› 2017, Vol. 50 ›› Issue (3): 168-173.DOI: 10.11930/j.issn.1004-9649.2017.03.168.06

基于改进萤火虫算法优化SVM的变电工程造价预测

宋宗耘¹, 牛东晓¹, 肖鑫利¹, 朱琳²

1. 华北电力大学经济与管理学院,北京 102206;
2. 国网新源控股有限公司技术中心,北京 100161

收稿日期:2016-12-05 出版日期:2017-03-20 发布日期:2017-03-17
作者简介:宋宗耘（1990—）,女,山东临沂人,博士研究生,从事电力负荷预测、技术经济评价等方面的研究。E-mail:songzongyun@126.com
基金资助:
国家自然科学基金资助项目（71471059）; 中央高校基本科研业务费专项资金资助项目（2016XS75; 2016XS73）

Substation Engineering Cost Forecasting Method Based on Modified Firefly Algorithm and Support Vector Machine

SONG Zongyun¹, NIU Dongxiao¹, XIAO Xinli¹, ZHU Lin²

1. School of Economic and Management, North China Electric Power University, Beijing 102206, China;
2. State Grid Xin Yuan Holdings Technology Company Limited, Beijing 100161, China

Received:2016-12-05 Online:2017-03-20 Published:2017-03-17
Supported by:
This work is supported by National Natural Science Foundation of China Project(No.71471059); Fundamental Research Funds for the Central Universities (No.2016XS75;No.2016XS73)

摘要/Abstract

摘要： 变电工程造价水平直接关系到电网工程的整体经济性,造价水平预测是控制造价、提高造价合理性的重要手段。在传统萤火虫算法的基础上,采用高斯扰动技术改进萤火种算法的位置更新公式,提高萤火从算法的寻优性能从而优化SVM预测模型的参数。通过Schaffer函数测试发现,高斯扰动萤火虫算法具有收敛速度快、搜索能力强等优点。实测结果表明：该模型具有较高的预测精度和有效性。

关键词: 萤火虫算法, 支持向量机, 高斯扰动, 变电工程, 造价预测

Abstract: The cost level of substation engineering is closely related to the integrated economy of power grid projects, and the cost level forecasting is a crucial tool for controlling cost and improving cost rationality. Based on the conventional firefly algorithm, the Gaussian Disturbance is introduced into the firefly algorithm to improve the update equation, which aims to improve the searching ability and optimize the SVM parameters. By operating the Schaffer testing function, it is discovered that the Gaussian disturbance firefly algorithm has better convergence rate and searching ability. The case study of substation engineering in Guangdong Province further proves that the proposed model has higher forecasting accuracy and effectiveness

Key words: firefly algorithm, support vector machine, Gaussian disturbance, substation engineering, cost forecasting

中图分类号:

TM714
TM744

宋宗耘, 牛东晓, 肖鑫利, 朱琳. 基于改进萤火虫算法优化SVM的变电工程造价预测[J]. 中国电力, 2017, 50(3): 168-173.

SONG Zongyun, NIU Dongxiao, XIAO Xinli, ZHU Lin. Substation Engineering Cost Forecasting Method Based on Modified Firefly Algorithm and Support Vector Machine[J]. Electric Power, 2017, 50(3): 168-173.

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https://www.electricpower.com.cn/CN/Y2017/V50/I3/168

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基于改进萤火虫算法优化SVM的变电工程造价预测

Substation Engineering Cost Forecasting Method Based on Modified Firefly Algorithm and Support Vector Machine

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基于改进萤火虫算法优化SVM的变电工程造价预测

Substation Engineering Cost Forecasting Method Based on Modified Firefly Algorithm and Support Vector Machine

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