基于灰色关联分析和K均值聚类的短期负荷预测

doi:10.12204/j.issn.1000-7229.2021.07.013

电力建设 ›› 2021, Vol. 42 ›› Issue (7): 110-117.doi: 10.12204/j.issn.1000-7229.2021.07.013

基于灰色关联分析和K均值聚类的短期负荷预测

黄冬梅¹, 庄兴科², 胡安铎¹, 孙锦中¹, 时帅², 孙园³, 唐振¹

1.上海电力大学电子与信息工程学院, 上海市 201306
2.上海电力大学电气工程学院,上海市 200090
3.上海电力大学数理学院, 上海市 201306

收稿日期:2020-10-11 出版日期:2021-07-01 发布日期:2021-07-09
通讯作者: 胡安铎
作者简介:黄冬梅(1964),女,教授,博士生导师,主要研究方向为海洋与电力时空信息技术;|庄兴科(1996),男,硕士研究生,主要研究方向为电力负荷预测;|孙锦中(1980),男,硕士,讲师,主要研究方向为电力时空信息技术;|时帅(1987),男,博士,讲师,主要研究方向为风电并网、新能源电力系统、海上风功率预测等;|孙园(1980),男,博士,副教授,主要研究方向为数据分析、挖掘和建模;|唐振(1997),男,硕士研究生,主要研究方向为电力负荷预测。
基金资助:
上海市科委地方院校能力建设项目(20020500700)

Short-Term Load Forecasting Based on Similar-Day Selection with GRA-K-means

HUANG Dongmei¹, ZHUANG Xingke², HU Anduo¹, SUN Jinzhong¹, SHI Shuai², SUN Yuan³, TANG Zhen¹

1. College of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China
2. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
3. College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China

Received:2020-10-11 Online:2021-07-01 Published:2021-07-09
Contact: HU Anduo
Supported by:
Local College Capacity Building Project of Shanghai Municipal Science and Technology Commission(20020500700)

摘要/Abstract

摘要：

在基于相似日的短期电力负荷预测技术中,相似日的选取影响着负荷预测精度,提出一种基于灰色关联分析(grey relation analysis,GRA)和K均值(K-means)聚类选取相似日的短期负荷预测模型。首先,采用灰色关联分析方法选取相似日粗集,再对相似日粗集的外部因素使用K均值聚类。然后,计算待预测日与聚类中心的欧氏距离,将距离最小一类作为最终相似日集合。最后,利用最终相似日集合训练长短期记忆(long-short term memory, LSTM)神经网络,进行负荷预测。与未采用相似日的LSTM模型和采用传统的灰色关联分析的LSTM模型相比,所提方法的平均绝对百分比误差(mean absolute percentage error,MAPE)分别降低了0.911%、0.637%。算例分析表明,采用GRA-K-means选取相似日可以有效提升短期电力负荷的预测精度。

关键词: 短期负荷预测, 灰色关联分析, K均值聚类, 相似日, LSTM神经网络

Abstract:

As the selection of similar days affecting the accuracy of load forecasting, a new short-term load forecasting model using GRA-K-means to select similar days is proposed in this paper. Firstly, GRA is used to select similar days to obtain rough sets of similar days. Secondly, K-means is used to cluster the external factors of rough sets of similar days, and then calculate the Euclidean distance between the predicated days and cluster center. The minimum distance is regarded as the final similar day set. Finally, the final similar day set is applied to train LSTM forecast model of neutral network to load forecast. Compared with LSTM model without similar days or LSTM model using traditional GRA, the MAPE in this paper is partly reduced by 0.911% and 0.637%. The analysis of the results shows that the model using GRA-K-means to select similar days can effectively improve the accuracy of short-term load forecasting.

Key words: short-term load forecasting, grey relation anlysis(GRA), K-means cluster, similar days, long-short term memory(LSTM) neural networks

中图分类号:

TM715

黄冬梅, 庄兴科, 胡安铎, 孙锦中, 时帅, 孙园, 唐振. 基于灰色关联分析和K均值聚类的短期负荷预测[J]. 电力建设, 2021, 42(7): 110-117.

HUANG Dongmei, ZHUANG Xingke, HU Anduo, SUN Jinzhong, SHI Shuai, SUN Yuan, TANG Zhen. Short-Term Load Forecasting Based on Similar-Day Selection with GRA-K-means[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(7): 110-117.

图/表 13

图1 某地区2014年上半年最高温度与日总负荷变化

Fig.1 Variations of the maximum temperature and total daily load in a certain area in the first half of 2014

表1 日期类型编码

Table 1 Date type encoding

表2 相似日影响因素的相关系数

Table 2 Correlation coefficients of influencing factors on similar days

图2 LSTM神经网络基本单元

Fig.2 Basic unit of LSTM neural network

图3 模型框架图

Fig.3 Diagram of the model frame

表3 模型的输入特征

Table 3 Input features of the model

图4 聚类评价指标

Fig.4 EEEEEEevaluation index of clustering

图5 综合聚类评价指标

Fig.5 Comprehensive evaluation index of clustering

表4 聚类中心与待预测日的欧氏距离

Table 4 Euclidean distance between cluster center and the day to be predicted

表5 形状系数平均值

Table 5 Average of shape coefficient

表6 不同相似日选取方法误差对比

Table 6 Error comparison of different similar-day selection methods

图6 不同相似日选取方法的预测负荷曲线

Fig.6 Load forecasting curves of different similar-day search methods

表7 不同模型误差对比

Table 7 Error comparison of different models

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基于灰色关联分析和K均值聚类的短期负荷预测

Short-Term Load Forecasting Based on Similar-Day Selection with GRA-K-means

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