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
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