基于实时电价和加权灰色关联投影的SVM电力负荷预测

被引：195

作者：

赵佩 ^{[1
]}

代业明 ^{[2
]}

机构：

[1] 青岛大学数学与统计学院

[2] 青岛大学商学院

来源：

电网技术 | 2020年 / 44卷 / 04期

基金：

中国博士后科学基金;

关键词：

电力负荷预测; 支持向量机; 实时电价; 加权灰色关联投影算法;

D O I：

暂无

中图分类号：

TM715 [电力系统规划]; TP181 [自动推理、机器学习];

学科分类号：

080802 [电力系统及其自动化]; 140502 [人工智能];

摘要：

精准的电力负荷预测有助于保障电力系统的安全调度和稳定运行,支持向量机作为一种良好的预测工具被广泛应用于电力负荷预测。随着智能电网的快速发展,实时电价成为电力负荷的重要影响因素,因此在应用支持向量机进行电力负荷预测时,引入实时电价这一影响因素,同时将加权灰色关联投影算法应用于节假日的历史负荷序列的选择,并采用改进的粒子群算法优化模型参数,最终得到一种实时电力负荷预测方法。以新加坡的电力数据为例进行实时电力负荷预测,并与通过反向传播神经网络得到的预测结果进行对比,结果表明所提出的方法具有较高的精确度和稳定性。

引用

页码：1325 / 1332

页数：8

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