Department of Electrical and Information Engineering University of Nairobi, Nairobi 301971, Kenya.
ScientificWorldJournal. 2024 Sep 2;2024:5568922. doi: 10.1155/2024/5568922. eCollection 2024.
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, _score of 0.9986, recall of 1, Matthews correlation coefficient of 0.9972, and _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 3 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.
实用程序面临着严重的窃电障碍，这需要创造性的方法来维持收入并提高运营效率。本研究提出了一种新颖的混合遗传人工蜂群算法-支持向量机分类器来检测窃电。该算法将人工蜂群算法的探索阶段与遗传算法的变异和交叉算子相结合，优化支持向量机的超参数，并将用户分类为欺诈或非欺诈用户。它利用来自利比里亚电力公司的 7270 行标记历史用电量数据，在 15 个独立运行中进行。该方法包括数据预处理、数据划分为训练集、验证集和测试集，比例为 80-10-10、Z 分数标准化、优化、训练、验证、测试和计算六个评估指标。与 13 种元启发式分类器和传统支持向量机进行比较。结果表明，遗传人工蜂群算法-支持向量机在六个评估指标中的表现优于 13 个竞争对手和标准支持向量机，准确率为 0.9986、精度为 0.9971、_score 为 0.9986、召回率为 1、马修斯相关系数为 0.9972 和_均值为 0.9987。此外，在 90%的时间里，Wilcoxon 秩和检验表明算法与其竞争对手之间存在统计学上的显著差异，证明了其优越性。平均运行时间为 4656 秒，是其竞争对手中最高的。尽管存在时间复杂度的权衡，但它在单峰和多峰基准测试函数上的出色表现，分别在 7 个和 6 个测试函数中排名第一，这为模型在平衡开发和探索、提高局部搜索和避免陷入局部最优方面的能力提供了重要的见解。这些发现解决了重要的元启发式优化差距，突出了该模型在检测窃电方面的潜力。
