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.