基于改进ABC和IDPC-MKELM的短期电力负荷预测

Short Term Power Load Forecasting Based on Improved ABC and IDPC-MKELM

为提高受外部因素影响敏感的短期电力负荷预测精度,提出了一种基于改进ABC优化密度峰值聚类和多核极限学习机的短期电力负荷预测方法。构建融合特征提取、人工蜂群算法(ABC)、密度峰值聚类(DPC)和核极限学习机(KELM)的短期电力负荷预测模型。针对ABC收敛效率不高的缺陷,设计新型蜜源搜索和蜜蜂进化方式,以提升改进ABC全局寻优能力;针对DPC截断距离与聚类中心人为设定的不足,定义邦费罗尼指数函数和聚类中心截断指标,并将改进的ABC应用于DPC参数优化过程,以实现DPC最佳聚类分析;针对KELM回归能力不强、参数选取难以确定的问题,设计多核加权KELM,并采用改进的ABC进行参数优化,以提高极限学习机预测精度。仿真结果表明,所提短期电力负荷预测方法更具有效性,平均误差低了约8.8%~39.8%。

In order to improve the accuracy of short-term power load forecasting which is sensitive to external factors,a short-term power load forecasting method based on improved ABC optimized density peak clustering and multiple kernel extreme learning machine is proposed.A short-term power load forecasting model is constructed that integrates feature extraction,artificial bee colony algorithm(ABC),density peak clustering(DPC)and kernel extreme learning machine(KELM).In view of the low convergence efficiency of ABC,a new honey source search and bee evolution method is designed to improve the global optimization capability of ABC.Aiming at the deficiency of artificial setting of DPC truncation distance and cluster center,Bonferroni exponential function and cluster center truncation index are defined,and the improved ABC is applied to DPC parameter optimization process to realize the best cluster analysis of DPC.Aiming at the problems of weak regression ability and difficult parameter selection of KELM,a multi-core weighted KELM is designed,and the improved ABC is used for parameter optimization to improve the prediction accuracy of limit learning machine.The simulation results show that the proposed short-term power load forecasting method is more effective,and the average error is reduced by 8.8%~39.8%.