Short-term load forecasting, profile identification, and customer segmentation: A methodology based on periodic time series

被引：179
作者：
Espinoza, M [1 ]
Joye, C
Belmans, R
De Moor, B
机构：
[1] Katholieke Univ Leuven, SCD Res Div, Dept Elect Engn, ESAT, B-3000 Louvain, Belgium
[2] Belgian Natl Grit Operator ELIA, B-1000 Brussels, Belgium
[3] Katholieke Univ Leuven, ELECTA Div, Dept Elect Engn, ESAT, B-3000 Louvain, Belgium
来源：
IEEE TRANSACTIONS ON POWER SYSTEMS | 2005年 / 20卷 / 03期
关键词：
load-forecasting; load modeling; time series; clustering methods;
D O I：
10.1109/TPWRS.2005.852123
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Results from a project in cooperation with the Belgian National Grid Operator ELIA are presented in this paper. Starting from a set of 245 time series, each one corresponding to four years of measurements from a HV-LV substation, individual modeling using Periodic Time Series yields satisfactory results for short-term forecasting or simulation purposes. In addition, we use the stationarity properties of the estimated models to identify typical daily customer profiles. As each one of the 245 substations can be represented by its unique daily profile, it is possible to cluster the 245 profiles in order to obtain a segmentation of the original sample in different classes of customer profiles. This methodology provides a unified framework for the forecasting and clustering problems.
引用
收藏
页码：1622 / 1630
页数：9
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