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Fuzzy Time-Series Model of Electric Power Consumption
Kazuhiro Ozawa*, ’Takahide Niimura** and Tomoaki Nakashima**
*Faculty of Economics, Hosei University Machida-shi, Tokyo 194-0298, Japan
**Department of Electrical and Computer Engineering, University of British Columbia Vancouver, B.C., Canada, V6T 1Z4
Received:March 12, 2000Accepted:May 20, 2000Published:May 20, 2000
Keywords:Time series, Autoregression, Possibility theory, Fuzzy numbers, Linear programming, Forecasting
Abstract
In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performance of the models are evaluated by vagueness and α-cuts. The proposed fuzzy Auoregression model represents the rich information of uncertainty that the original data contain, and it can be a powerful tool for flexible decision-making with uncertainty. The fuzzy AR model can also be constructed in relatively simple procedure compared with the conventional approaches.
Cite this article as:K. Ozawa, &. Niimura, and T. Nakashima, “Fuzzy Time-Series Model of Electric Power Consumption,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.3, pp. 188-194, 2000.Data files:
