Performance Assessment of SARIMA Model with Holt aWinteras Trend and Additive Seasonality Smoothing Method on Forecasting Electricity Production of Australia an Empirical Study

Authors

  • Md. Matiur Rahman Molla

Keywords:

electricity production, seasonal ARIMA, smoothing, forecasting, time series analysis

Abstract

Australia is a leading developed country which is indispensable a proper planning and management of power generation. To take a unique planning decision forecasting of electricity production is badly in need so that electricity generation copes with the demand of the electricity smoothly. The main task of this study is to assess the performance of two time series models in forecasting electricity generation in Australia. Two time series forecasting methods such as ARIMA and Holt-Winter#x2019;s additive trend and seasonality smoothing methods are considered. Applying Theil#x2019;s U-statistic as the key performance measure, the study concludes that Holtwinter#x2019;s method is more appropriate model.

How to Cite

Md. Matiur Rahman Molla. (2016). Performance Assessment of SARIMA Model with Holt aWinteras Trend and Additive Seasonality Smoothing Method on Forecasting Electricity Production of Australia an Empirical Study. Global Journals of Research in Engineering, 16(J2), 7–11. Retrieved from https://engineeringresearch.org/index.php/GJRE/article/view/100722

Performance Assessment of SARIMA Model with Holt aWinteras Trend and Additive Seasonality Smoothing Method on Forecasting Electricity Production of Australia an Empirical Study

Published

2016-03-15