Information Sciences: an International Journal
Structure identification of fuzzy model
Fuzzy Sets and Systems
Fuzzy Sets and Systems
S-curve regression model in fuzzy environment
Fuzzy Sets and Systems
Forecasting of the electric energy demand trend and monthly fluctuation with neural networks
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The well-known fuzzy rule-based Takagi-Sugeno-Kang (TSK) model is combined with a set of fuzzy regressions (FR) to investigate the impact of the climate change on the electricity consumption duration. The electricity demand forecasts in the short-terms have a vital application in electricity markets. Knowing that the energy is a product of the relation between the climate change and the average consumption duration of the peak load. The paper introduces a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors in the consequent part to model effects of the climate change on the electricity demand. However, a simplified version of the model is applied to daily data of the average temperature in Tehran, 2004. First, based on an initially fitted nonlinear curve, an optimization model is employed to cluster data into three groups of cold, temperate and hot. The fuzzy data have been expanded to reduce the temperature volatile property. Then the relation is estimated by the fuzzy regressions in company with the TSK model. Numerical results show high efficiency of the proposed combined fuzzy model, as well as a minor decrease in the average absolute error.