Attention Short-Term Forecasting Method of Distribution Load Based on Multi-Dimensional Clustering

doi:10.16183/j.cnki.jsjtu.2021.263

Abstract

Abstract:

Due to the difference in load characteristics and influencing factors in large-scale distribution transformer load forecasting, if all the distribution transformers share a unified model, the prediction accuracy is low, and if the model is built for each distribution transformer, the computational resources will be excessively consumed. An Attention-LSTM short-term forecasting method of distribution load based on multi-dimensional clustering is proposed. The non-parametric kernel method is used to perform probability fitting on the daily load characteristics to form a typical daily load sequence. Improved two-level clustering is applied for load clustering, taking the Euclidean warping distance and influence factors as the similarity evaluation criteria. AP clustering is utilized for obtaining similar time-series, and training sets are formed to train the Attention-LSTM model. Different Attention-LSTM models are obtained by training for different distribution load types and time-series. The effectiveness and practicability of the method proposed are verified by the load data and meteorological data of a municipal distribution network. The accuracy rate is increased by 2.75% and the efficiency is increased by 616.8%.

Key words: short-term load forecasting, daily load sequence, load clustering, similar time-series, long short-term memory network

CLC Number:

TM715

ZHONG Guangyao, TAI Nengling, HUANG Wentao, LI Ran, FU Xiaofei, JI Kunhua. Attention Short-Term Forecasting Method of Distribution Load Based on Multi-Dimensional Clustering[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1532-1543.

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时段		指标	定义	变量
全天	00:00—24:00	负荷率	P_av/P_max	y₁
		日峰谷差率	(P_max-P_min)/P_max	y₂
峰期	08:00—11:00	峰期负载率	P_{av, peak}/P_av	y₃
	18:00—21:00
平期	06:00—08:00	平期负载率	P_{av, sh}/P_av	y₄
	11:00—18:00
	21:00—22:00
谷期	22:00—24:00	谷期负载率	P_{av, val}/P_av	y₅
	00:00—06:00

天气	标记值
晴	0
多云	0.2
阴	0.4
小雨	0.6
中雨	0.8
大雨	1.0
工作日	0
周末	0.5
节假日	1

k	DBI
2	0.91
3	1.37
4	1.12
5	0.75
6	1.05
7	1.41
8	1.10
9	1.43
10	1.54
11	0.82
12	0.97

类别	信息熵
第1类	0.216
第2类	0.483
第3类	0.149
第4类	0.134
第5类	0.201

组别	MAPE/%						RMSE/KW						训练时间/s	泛化时间/s
组别	第1类	第2类	第3类	第4类	第5类	第6类	第1类	第2类	第3类	第4类	第5类	第6类	训练时间/s	泛化时间/s
实验组	1.39	1.56	5.27	1.31	1.50	1.21	0.043	0.047	0.159	0.040	0.047	0.037	93.7	0.076
对比组1	5.21	4.71	7.32	5.33	5.69	4.23	0.157	0.138	0.226	0.161	0.179	0.132	655.6	0.080
对比组2	3.35	3.47	6.12	4.15	4.22	3.09	0.104	0.106	0.189	0.128	0.129	0.093	671.7	0.079
对比组3	4.61	4.15	7.08	5.11	4.39	3.91	0.141	0.126	0.221	0.151	0.130	0.116	6.53	0.072