Electricity Demand Forecasting Using HWT model with Fourfold Seasonality

被引:5
|
作者
Huang, Jiangshuai [1 ]
Srinivasan, Dipti [2 ]
Zhang, Dan [3 ]
机构
[1] Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China
[2] Natl Univ Singapre, Dept Elect & Comp Engn, Singapore, Singapore
[3] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou, Zhejiang, Peoples R China
关键词
Electricity demand forecasting; HWT exponential smoothing model; Fourfold seasonality; NEURAL-NETWORK; EXPERT-SYSTEM; TIME-SERIES; WEATHER; IMPACT;
D O I
10.1109/ICCAIRO.2017.55
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Seasonality methods have been developed to model the intraday, intraweek and intrayear seasonal cycles of the electricity load data in one-day ahead electricity demand forecasting. In this paper, we investigate the short-term modeling and forecasting of electricity demand where an intramonth cycle has also been discovered. Thus based on the intramonth cycle, a new mathematical modeling scheme is developed for HWT exponential smoothing model to accommodate the intramonth seasonal cycle and by using six years of Singapore data. We show that fourfold seasonal method outperforms the triple seasonal method in Singapore.
引用
收藏
页码:254 / 258
页数:5
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