融合GSO算法与AFSA算法的人工智能电力系统预测模型设计  

Design of Artificial Intelligence Power System Prediction Model by Fusing GSO Algorithm and AFSA Algorithm

作　　者：郑志娴[1] 郑晶[2] Zheng Zhixian;Zheng Jing(College of Information and Intelligent Transportation,Fujian Chuanzheng Communications College,Fuzhou,Fujian 350007,China;Department of Electronic and Information Science,Fujian Jiangxia University,Fuzhou,Fujian 350108,China)

机构地区：[1]福建船政交通职业学院信息与智慧交通学院,福建福州350007 [2]福建江夏学院电子信息科学学院,福建福州350108

出　　处：《黑龙江工业学院学报（综合版）》2024年第3期115-120,共6页Journal of Heilongjiang University of Technology(Comprehensive Edition)

基　　金：福建省中青年教师教育科研项目“面向移动应用跨平台开发框架的研究与实现”(项目编号:JAT210719)。

摘　　要：为了解决传统统计模型在电力系统负荷预测中存在的稳定性差、使用率低等情况,研究将人工智能引入到了电力系统的统计模型中。研究创新地将人工萤火虫算法和人工鱼群算法进行优化和融合,将其用于构建电力系统负荷预测模型。首先对人工萤火虫算法进行优化,然后将优化后的人工萤火虫算法与人工鱼群算法进行融合用于构建电力负荷预测模型,最后利用仿真实验来验证预测模型的性能。结果表明,通过预测模型的归一化处理,节点电压的波动范围明显更平稳,其波动范围分布在[0.961~1.00pu]。同时预测模型在迭代至66次获得了最优解,也明显优于对比算法。这说明融合人工萤火虫算法和人工鱼群算法的人工智能电力系统负荷预测模型在准确性和稳定性方面表现出优越性。In order to solve the poor stability and low utilization of traditional statistical models in power system load forecasting,the study introduces artificial intelligence into the statistical model of power system.The study innovatively optimizes and integrates the artificial firefly algorithm and the artificial fish swarm algorithm,and uses them to construct the power system load forecasting model.Firstly,the artificial firefly algorithm is optimized,and then the optimized artificial firefly algorithm is fused with the artificial fish swarm algorithm for constructing the power load prediction model,and finally the performance of the prediction model is verified by using simulation experiments.The results show that the fluctuation range of the node voltage is obviously smoother through the normalization of the prediction model,and its fluctuation range is distributed in[0.961-1.00].Meanwhile the prediction model obtained the optimal solution in iterations up to 66 times,which is also significantly better than the comparison algorithm.This indicates that the artificial intelligence power system load prediction model incorporating the artificial firefly algorithm and the artificial fish swarm algorithm shows superiority in terms of accuracy and stability.

关 键 词：GSO算法 AFSA算法 人工智能 电力系统 负荷预测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM715[自动化与计算机技术—控制科学与工程]