华东师范大学学报（自然科学版） >

2021 , Vol. 2021 >Issue 5: 146 - 156

DOI: https://doi.org/10.3969/j.issn.1000-5641.2021.05.013

数据分析与应用

基于自适应竞争的均衡优化电力系统客户分类

  • 郑思达 ,
  • 刘岩 ,
  • 杨晓坤 ,
  • 戚成飞 ,
  • 袁培森
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  • 1. 国网冀北电力有限公司 计量中心, 北京　100045
    2. 南京农业大学 人工智能学院, 南京　210095
郑思达, 男, 硕士, 工程师, 研究方向为电力数据应用、用电信息采集. E-mail: 549186079@qq.com

收稿日期: 2021-08-07

  网络出版日期: 2021-09-28

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Adaptive competitive equilibrium optimizer for power system customer classification

  • Sida ZHENG ,
  • Yan LIU ,
  • Xiaokun YANG ,
  • Chengfei QI ,
  • Peisen YUAN
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  • 1. Metrology Center, State Grid Jibei Electric Power Supply Company, Beijing　100045 China
    2. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing　210095, China

Received date: 2021-08-07

  Online published: 2021-09-28

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摘要

对电力系统客户的精确分类可为客户提供良好的差异化管理和个性化服务. 针对客户分类问题, 提出了一种基于均衡优化与极限学习机的分类方法. 该方法中提出了一种自适应竞争机制来平衡均衡优化的全局探索与局部挖掘能力, 从而有效提升了均衡优化搜索最优解的性能. 之后, 将提出的均衡优化集成极限学习机对电力系统的客户进行分类. 通过真实数据集上的实验表明, 在不同的分类指标下, 所提出的均衡优化集成极限学习机都具有良好的预测效果, 可为电力系统客户管理与服务提供有效的技术手段.

关键词： 均衡优化; 极限学习机; 电力系统; 客户分类

本文引用格式

郑思达 , 刘岩 , 杨晓坤 , 戚成飞 , 袁培森 . 基于自适应竞争的均衡优化电力系统客户分类[J]. 华东师范大学学报（自然科学版）, 2021 , 2021(5) : 146 -156 . DOI: 10.3969/j.issn.1000-5641.2021.05.013

Abstract

Accurate classification of power system customers can enable differentiated management and personalized services for customers. In order to address the challenges associated with accurate customer classification, this paper proposes a classification method based on an equilibrium optimizer and an extreme learning machine. In this method, an adaptive competition mechanism is proposed to balance the global exploration and local mining ability of an equilibrium optimizer, improving the performance of algorithms in finding optimal solutions. Thereafter, the proposed equilibrium optimizer is integrated with an extreme learning machine to classify the customers of a power system. Experiments on real data sets showed that the proposed algorithm integrated with an extreme learning machine offers more accurate performance for different classification indexes; hence, the proposed method can provide an effective technical means for power system customer management and service.

Key words： equilibrium optimizer; extreme learning machine; power system; customer classification

参考文献

1 吴贺. 传统金融机构转型与变革的研究. 中国集体经济, 2021, 21, 96- 97.
2 WANG P, ZHANG P Y. Classification and management of electricity market customer considering demand response in China [C]//2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). IEEE, 2017: 1-5. DOI: 10.1109/EEEIC.2017.7977664.
3 吴蕊, 张安勤, 田秀霞, 等. 基于改进K-means的电力数据异常检测算法 . 华东师范大学学报(自然科学版), 2020, (4): 79- 87.
4 陈聿, 田博今, 彭云竹, 等. 联合手肘法和期望最大化的高斯混合聚类电力系统客户分群算法. 计算机应用, 2020, 40, (11): 3217- 3223.
5 BARMAN M, DEV CHOUDHURY N B. A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam, India. Sustainable Cities and Society, 2020, 61, 102311.
6 LIU L Y, LIU D R, SUN Q, et al. Forecasting power output of photovoltaic system using a BP network method. Energy Procedia, 2017, 142, 780- 786.
7 WANG Z, WANG B, LIU C, et al. Improved BP neural network algorithm to wind power forecast. The Journal of Engineering, 2017, 13, 940- 943.
8 HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70 (1/2/3): 489- 501.
9 CHEN Z Y, GRYLLIAS K, LI W H. Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mechanical Systems and Signal Processing, 2019, 133, 106272.
10 SHARIATI M, TRUNG N T, WAKIL K, et al. Estimation of moment and rotation of steel rack connections using extreme learning machine. Steel and Composite Structures, 2019, 31 (5): 427- 435.
11 CHEN Y, PI D C. Novel fruit fly algorithm for global optimisation and its application to short-term wind forecasting. Connection Science, 2019, 31 (3): 244- 266.
12 JAHROMI A N, HASHEMI S, DEHGHANTANHA A, et al. An improved two-hidden-layer extreme learning machine for malware hunting. Computers & Security, 2020, 89, 101655.
13 LIU X Y, HUANG H Z, XIANG J W. A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine. Knowledge-Based Systems, 2020, 195, 105653.
14 ZENG N Y, QIU H, WANG Z D, et al. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing, 2018, 320, 195- 202.
15 XIA X. Study on the application of BP neural network in air quality prediction based on adaptive chaos fruit fly optimization algorithm [J]. MATEC Web of Conferences, 2021, 336: 07002.
16 UTHAYAKUMAR J, METAWA N, SHANKAR K, et al. Financial crisis prediction model using ant colony optimization. International Journal of Information Management, 2020, 50, 538- 556.
17 GAO W Y, SU C. Analysis of earnings forecast of blockchain financial products based on particle swarm optimization. Journal of Computational and Applied Mathematics, 2020, 372, 112724.
18 RUAN X M, ZHU Y Y, LI J, et al. Predicting the citation counts of individual papers via a BP neural network. Journal of Informetrics, 2020, 14 (3): 101039.
19 SALGOTRA R, SINGH U, SINGH S, et al. Self-adaptive salp swarm algorithm for engineering optimization problems. Applied Mathematical Modelling, 2021, 89, 188- 207.
20 LONG W, JIAO J J, LIANG X M, et al. An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Engineering Applications of Artificial Intelligence, 2018, 68, 63- 80.
21 MIRJALILI S. SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 2016, 96, 120- 133.
22 FARAMARZI A, HEIDARINEJAD M, STEPHENs B, et al. Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 2020, 191, 105190.
23 CHEN Y, PI D C, XU Y. Neighborhood global learning based flower pollination algorithm and its application to unmanned aerial vehicle path planning. Expert Systems with Applications, 2021, 170, 114505.
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