Clustering based Short Term Load Forecasting using Artificial Neural Network

被引：0
|
作者：
Jain, Amit [1 ]
Satish, B. [1 ]
机构：
[1] Int Inst Informat Technol, Power Syst Res Ctr, Hyderabad, Andhra Pradesh, India
来源：
2009 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION, VOLS 1-3 | 2009年
关键词：
Artificial Neural Network; Back Propagation Algorithm; Clustering; Short Term Load Forecasting; ELECTRICITY DEMAND; AUTOREGRESSIVE MODELS; POWER-SYSTEM; TEMPERATURE;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
A novel clustering based Short Term Load Forecasting (STLF) using Artificial Neural Network (ANN) to forecast the 48 half hourly loads for next day is presented in this paper. The proposed architecture uses the historical load and temperature to forecast the next day load. It is trained using back propagation algorithm and tested. The daily average load of each day for all the training patterns and testing patterns is calculated and the patterns are clustered using a threshold value between the daily average load of the testing pattern and the daily average load of the training patterns. The results obtained from neural network are presented and the results show that the clustering based approach is more accurate.
引用
收藏
页码：1210 / 1216
页数：7
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