Nonlinear ARIMAX model for long –term sectoral demand forecasting

• Main Authors: Najmeh Neshat, Hengameh Hadian, Matineh Behzad
• Format: Article
• Language: English
• Published: Growing Science 2018-06-01
• Series: Management Science Letters
• Subjects: Nonlinear Forecasting Model; Times-Series Analysis; Peak Demand
• Online Access: http://www.growingscience.com/msl/Vol8/msl_2018_45.pdf

With the rapid increase of energy demand, it is becoming increasingly important to obtain accurate energy demand forecasts. To incorporate long time causal relationships, autoregressive with exoge-nous regression components models have received increasing attention from many researchers in this field. These are linear models applied through hybrid methodology of time series and econo-metrics, however, some recent studies find evidences that nonlinear models outperform over linear ones in long term peak demand forecasting. This paper proposed a nonlinear Auto Regressive Integrated Moving Average with Exogenous Inputs (N-ARIMAX) model to forecast sectoral peak demand using a case study of Iran. The results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the existing models.