Applying fuzzy grey modification model on inflow forecasting
Journal
Engineering Applications of Artificial Intelligence
Journal Volume
25
Journal Issue
4
Pages
734-743
Date Issued
2012
Author(s)
Abstract
This paper investigates a modified grey model for forecasting the inflow of a reservoir. The integral form of the background value is employed for the original grey model, GM(1,1), to improve accuracy and applicability. Thereafter, the Fourier series is altered to handle extreme values with regard to prediction; exponential smoothing is used to improve the drawbacks of the prediction delay phenomenon. Finally, we are hybridised as the ultimate grey model with outstanding prediction accuracy, namely EFGM(1,1). As a typhoon causes significant changes in the inflow of a reservoir, this paper applies the fuzzy membership function for dealing with it during the flood season to construct the fuzzy grey modification model, FEFGM(1,1). Results of grey models are compared with those of the Autoregressive Integrated Moving Average (ARIMA). By evaluating different indices, the errors of the predicted extreme value of EFGM(1,1) perform better than those of GM(1,1) and ARIMA, however worse than that of FEFGM(1,1). The final FEFGM(1,1) shows high precision with regard to reservoir inflow prediction during typhoons with combined effects of fuzzy, exponential smoothing, Fourier series. © 2012 Elsevier Ltd. All rights reserved.
SDGs
Type
journal article