基于组合优化理论的用电量预测模型

Electricity Consumption Prediction Based on Combination Optimization Theory

为了提高用电量的预测精度,提出了一种基于组合优化理论的用电量预测模型(AFSA-LSSVM).首先相空间重构用电量学习样本,然后将学习样本输入到最小二乘支持向量机进行训练,并采用人工鱼群算法优化LSSVM参数,建立最优的用电量预测模型,最后采用仿真实验对模型性能进行测试.结果表明,相对于对比模型,AFSA-LSSVM可以准确刻画用电量的变化趋势,提高用电量的预测精度,预测结果更加可靠,可以为决策者提供有价值决策信息.

In order to improve the prediction precision, a novel electricity consumption prediction model is proposed based on combination optimization theory. Firstly, the learning samples is obtained by phase space reconstruction. Then the learning samples are input into least square support vector machine and train, which the parameters of model are optimized by artificial fish swarm algorithm, and electricity consumption prediction model is established. Finally, the performance of model is test by simulation experiment. The results show that the proposed model can describe electricity consumption change rule, and improve the prediction precision.