| 郝文斌,孟志高,张 勇,谢 波,彭 攀,卫佳奇.基于SOM特征聚类及RBF神经网络的电力负荷预测方法研究[J].电力需求侧管理,2024,26(2):49-54 |
| 基于SOM特征聚类及RBF神经网络的电力负荷预测方法研究 |
| Research on power load forecasting method based on feature clustering of SOM and RBF neural network |
| 投稿时间：2023-12-11 修订日期：2024-01-19 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 02. 008 |
| 中文关键词: 负荷预测 自组织映射聚类 径向基函数神经网络 粒子群优化算法 |
| 英文关键词: load forcasting self-organizing mapping clustering radial basis function neural network particle swarm optimization algorithm |
| 基金项目:国网四川省电力公司成都供电公司科研项目（521904220001） |
|
| 摘要点击次数: 0 |
| 全文下载次数: 0 |
| 中文摘要: |
| 为了提高电力系统负荷预测的精度,维护电力系统运行的安全稳定性,提出一种基于特征向量的自组织映射聚类和改进的径向基函数神经网络相结合的电力负荷预测模型。通过提取能够体现每日电力负荷特性的特征向量,对样本进行聚类,采用具有相似特征的数据作为神经网络的训练样本,提高了样本规律性。采用粒子群算法（particle swarm optimization,PSO）修正神经网络粒子群速度及位置,以克服梯度下降、局部最优等问题对网络预测精度的影响。基于某地配电网电力负荷数据,验证了所提模型的有效性及良好的适应性。 |
| 英文摘要: |
| To improve the accuracy of power system load forecasting and maintain the safety and stability of power system operation, a combination of self-organizing maps（SOM）clustering based on feature vector and improved radial basis function（RBF）neural network for power load forecasting model is proposed. The samples are clustered by extracting feature vectors that reflect the characteristics of the daily electric load. Data with similar features are used as training samples for the neural network to improve sample regularity. To overcome the effects of gradient descent and local optimum on the network prediction accuracy, the particle swarm optimization（PSO）algorithm is used to modify the neural network particle swarm velocity and position. The validity and good adaptability of the proposed model are verified based on power load data of distribution network in an area. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |