用于及时发现窃电行为的遗传人工蜂鸟算法-支持向量机。

Q2 Environmental Science
The Scientific World Journal Pub Date : 2024-09-02 eCollection Date: 2024-01-01 DOI:10.1155/2024/5568922
Emmanuel Gbafore, Davies Rene Segera, Cosmas Raymond Mutugi Kiruki
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引用次数: 0

摘要

电力公司面临着严重的窃电障碍,这就要求采用创新方法来维持收入和提高运营效率。本研究提出了一种新型混合遗传人工蜂鸟算法-支持向量机分类器来检测窃电行为。所提出的算法将人工蜂鸟算法的探索阶段与遗传算法的突变和交叉算子相结合,以优化支持向量机的超参数,并将用户分为欺诈和非欺诈用户。它利用利比里亚电力公司提供的 7,270 行标注的历史用电数据进行了 15 次独立运行。该方法包括数据预处理、按 80-10-10 的比例将数据分成训练集、验证集和测试集、z-score 归一化、优化、训练、验证、测试和六个评估指标的计算。它的性能与 13 种元启发式分类器和传统的支持向量机进行了比较。结果表明,在六项评估指标中,遗传人工蜂鸟算法-支持向量机的准确度为 0.9986,精确度为 0.9971,f_score 为 0.9986,召回率为 1,马修斯相关系数为 0.9972,g_mean 为 0.9987,优于 13 个对手和标准支持向量机。此外,在 90% 的情况下,Wilcoxon 秩和检验显示该算法与其竞争对手之间存在显著的统计学差异,这证明了该算法的优越性。平均运行时间为 4656 秒,在竞争对手中排名第三。尽管在时间复杂度上有所权衡,但该算法在单模态和多模态基准测试功能上的出色表现,分别在 7 个基准测试功能中的 7 个和 6 个基准测试功能中的 5 个中名列前茅,为该模型平衡开发与探索、改进局部搜索和避免陷入局部最优的能力提供了重要启示。这些发现弥补了元启发式优化的重要不足,凸显了该模型在窃电检测方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection.

Utilities face serious obstacles from power theft, which calls for creative ways to maintain income and improve operational effectiveness. This study presents a novel hybrid genetic artificial hummingbird algorithm-support vector machine classifier to detect power theft. The proposed algorithm combines the artificial hummingbird algorithm exploration phase with the genetic algorithm's mutation and crossover operators, to optimize the support vector machine's hyperparameters and categorize users as fraudulent or nonfraudulent. It utilizes 7,270 rows of labeled historical electricity consumption data from the Liberia Electricity Corporation over 15 independent runs. The methodology entailed data preprocessing, data split into training, validation, and testing sets in an 80-10-10 ratio, z-score normalization, optimization, training, validation, testing, and computation of six evaluation metrics. Its performance is compared with 13 metaheuristic classifiers and the conventional support vector machine. Findings indicate that the genetic artificial hummingbird algorithm-support vector machine outperforms the 13 rivals and the standard support vector machine in the six assessment measures with an accuracy score of 0.9986, precision of 0.9971, f_score of 0.9986, recall of 1, Matthews correlation coefficient of 0.9972, and g_mean of 0.9987. Furthermore, 90% of the time, Wilcoxon rank-sum tests revealed statistically significant differences between the algorithm and its rivals, demonstrating its superiority. The average run time is 4,656 seconds, the 3rd highest among its competitors. Despite the time complexity trade-off, its excellent performance on the unimodal and multimodal benchmark test functions, placing joint best in 7 out of 7 and 5 out of 6, respectively, provides important insights into the model's capacity to balance exploitation and exploration, improve local search, and avoid becoming stuck in the local optimum. These findings address important metaheuristic optimization gaps highlighting the model's potential for power theft detection.

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来源期刊
The Scientific World Journal
The Scientific World Journal 综合性期刊-综合性期刊
CiteScore
5.60
自引率
0.00%
发文量
170
审稿时长
3.7 months
期刊介绍: The Scientific World Journal is a peer-reviewed, Open Access journal that publishes original research, reviews, and clinical studies covering a wide range of subjects in science, technology, and medicine. The journal is divided into 81 subject areas.
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