摘要
针对传统电力负荷预测中存在的对非线性关系的不适应问题,提出了基于Levy飞行策略的改进粒子群优化算法。即利用离散化方式优化原始数据集,经数据挖掘得出3个关键性参数,接着通过引入Levy飞行策略,该算法可在优化过程中更灵活地调整粒子位置,有效提高了电力负荷预测的准确性和全局搜索性能。并通过实验证实了LPSO算法的优越性,为电力系统运营提供了更可靠的预测工具。
In response to the problem of non adaptation to nonlinear relationships in traditional power load forecasting,an improved particle swarm optimization algorithm based on Levy flight strategy is proposed.Firstly,the original dataset is optimized using a discretization approach,and three key parameters are identified through data mining.Then,by introducing the Levy flight strategy,the algorithm adjusts particle positions more flexibly during the optimization process,effectively improving the accuracy and global search performance of power load forecasting.The superiority of the LPSO algorithm has been confirmed through experiments,providing a more reliable prediction tool for power system operation.
出处
《今日自动化》
2024年第4期99-101,共3页
Automation Today
关键词
配电网
大数据
数据分析
负荷预测
distribution network
big data
data analysis
load forecasting
