DocumentCode :
1740390
Title :
Next day load curve forecasting using self organizing map
Author :
Senjyu, Tomonobu ; Tamaki, Yoshinori ; Uezato, Katsumi
Author_Institution :
Ryukyus Univ., Okinawa, Japan
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
1113
Abstract :
In this paper, we propose a new prediction scheme using self organizing map for next day load curve forecasting. In the proposed scheme, we select several similar days corresponding to forecasted day using a Kohonen network which is a representative of self organizing map, and we forecast the next day load curve by averaging selected similar days. Therefore, we do not need complex algorithm and structure such as supervised neural network, genetic algorithm (GA) and fuzzy inference, and we can forecast next day load curve easily. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan
Keywords :
load forecasting; power system analysis computing; self-organising feature maps; Japan; Kohonen network; Okinawa Electric Power Company; next day load curve forecasting; prediction scheme; selected similar days averaging; self organizing map; Casting; Fuzzy neural networks; Genetic algorithms; Inference algorithms; Load forecasting; Neural networks; Neurons; Organizing; Power system control; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power System Technology, 2000. Proceedings. PowerCon 2000. International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-6338-8
Type :
conf
DOI :
10.1109/ICPST.2000.897176
Filename :
897176
Link To Document :
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