基于ICSO-SOM-ELM的电力业扩项目工期预测

doi:10.3969/j.issn.1007-7375.2023.02.007

工业工程 ›› 2023, Vol. 26 ›› Issue (2): 59-66.doi: 10.3969/j.issn.1007-7375.2023.02.007

基于ICSO-SOM-ELM的电力业扩项目工期预测

林镜星¹, 周鑫¹, 谢志炜¹, 许炫淙², 张铮²

1. 广东电网有限责任公司广州供电局, 广东广州 510000;
2. 广东工业大学自动化学院, 广东广州 510006

收稿日期:2022-01-17 发布日期:2023-05-05
通讯作者: 许炫淙(1998-),男,广东省人,硕士研究生,主要研究方向为电力系统智能应用。E-mail:864988668@qq.com E-mail:864988668@qq.com
作者简介:林镜星(1978-),男,广东省人,高级工程师,硕士,主要研究方向为配网工程管理
基金资助:
国家自然科学基金资助项目(61876040)；南方电网科技资助项目(080008KK52200010)

Duration Prediction of Power Business Expansion Project Based on ICSO-SOM-ELM

LIN Jingxing¹, ZHOU Xin¹, XIE Zhiwei¹, XU Xuancong², ZHANG Zheng²

1. Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd, Guangzhou 510000, China;
2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Received:2022-01-17 Published:2023-05-05

摘要/Abstract

摘要： 针对电力业扩项目时长的不确定性，提出一种自组织映射网络聚类、改进纵横交叉算法优化极限学习机权值阈值的ICSO-SOM-ELM电力业扩项目工期预测模型。首先基于项目预算费用与节点数，采用自组织映射网络对电力业扩项目数据进行二次聚类，初步降低原始数据集的混乱性。其次，提出基于邻域种群交叉变异机制的改进纵横交叉算法，并将其用于优化极限学习机模型的权值阈值，得到最优ELM预测模型。最后，针对电力业扩项目二次聚类数据，分别采用ICSO-ELM预测模型对项目时长进行预测。以某供电局业扩数据进行实验，验证所提模型的有效性，所提出的ICSO-SOM-ELM预测模型优于其他预测模型，可为供电公司的业扩项目工期计划制定提供科学性的建议。

关键词: 电力业扩项目, 工期预测, 自组织映射网络, 改进纵横交叉算法, 极限学习机

Abstract: Aiming at the uncertainty of power business expansion project duration, an ICSO-SOM-ELM prediction model of power business expansion project duration is proposed based on self-organizing map network clustering and improved crisscross algorithm to optimize the weight threshold of extreme learning machine. Firstly, based on the project budget cost and the number of nodes, the self-organizing map network is used to secondarily cluster the data of power business expansion project, so as to preliminarily reduce the confusion of the original data set. Secondly, an improved crisscross algorithm based on the mechanism of neighborhood population crossover and mutation is proposed, which is used to optimize the weight threshold of an extreme learning machine model to obtain the optimal ELM prediction model. Finally, according to the secondary clustering data of power business expansion projects, an ICSO-ELM prediction model is used to predict the project duration. An experiment is conducted with the business expansion data of a power supply company. Results show that the proposed ICSO-SOM-ELM prediction model is better than other ones, verifying its effectiveness and providing scientific suggestions for duration planning of power supply companies’ business expansion projects.

Key words: power business expansion project, duration prediction, self-organizing map network, improved crisscross algorithm, extreme learning machine

中图分类号:

TM732

林镜星, 周鑫, 谢志炜, 许炫淙, 张铮. 基于ICSO-SOM-ELM的电力业扩项目工期预测[J]. 工业工程, 2023, 26(2): 59-66.

LIN Jingxing, ZHOU Xin, XIE Zhiwei, XU Xuancong, ZHANG Zheng. Duration Prediction of Power Business Expansion Project Based on ICSO-SOM-ELM[J]. Industrial Engineering Journal, 2023, 26(2): 59-66.

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基于ICSO-SOM-ELM的电力业扩项目工期预测

Duration Prediction of Power Business Expansion Project Based on ICSO-SOM-ELM

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