2025年5月13日 周二
首页 > 过刊浏览>2022年第37卷第1期 >90-95. DOI:10.19781/j.issn.1673-9140.2022.01.011
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基于改进K-means算法的电力短期负荷预测方法研究
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荀超(1984-)女,硕士,高级工程师,主要从事电力负荷预测分析研究;E-mail:tdksks@163.com

中图分类号:

TM715

基金项目:

国家自然科学基金(51177107)；国家电网有限公司科技项目(52022319003P)


Research on short-term power load forecasting method based on improved K-means algorithm
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    摘要:

    现有方法预测电力短期负荷时忽略了对其进行聚类优化处理,导致预测耗时较长,短期负荷预测精度偏低。为此,提出一种基于改进K-means算法的电力短期负荷预测方法。该方法利用改进后的K-means算法聚类处理电力负荷大数据,使用聚类后获得的训练样本构建循环神经网络RNN拓扑结构,然后通过对RNN神经网络模型设置最优权值,实现电力负荷的短期预测。实验结果表明,所提方法具有高预测效率和高短期负荷预测精准度。

    Abstract:

    The existing methods ignore the clustering optimization process when predicting the short-term load of electricity, which leads to a long prediction time and a low accuracy of short-term load prediction. Therefore, a short-term load forecasting method based on improved K-means algorithm is proposed. This method uses the improved K-means algorithm to cluster the big data of power load, uses the training samples obtained after clustering to construct the RNN topology structure of the recurrent neural network. Then the optimal weights are set for the RNN neural network model to realize short-term forecast of the power load. The experimental results show that the proposed method has high forecasting efficiency and high short-term load forecasting accuracy.

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引用本文

荀超,陈伯建,吴翔宇,等.基于改进K-means算法的电力短期负荷预测方法研究[J].电力科学与技术学报,2022,37(1):90-95.
XUN Chao, CHEN Bojian, WU Xiangyu, et al. Research on short-term power load forecasting method based on improved K-means algorithm[J]. Journal of Electric Power Science and Technology,2022,37(1):90-95.

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