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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’s additive trend and seasonality smoothing methods are considered. Applying Theil’s U-statistic as the key performance measure, the study concludes that Holtwinter’s method is more appropriate model.
Md. Matiur Rahman Molla. 2016. \u201cPerformance Assessment of SARIMA Model with Holt aWinteras Trend and Additive Seasonality Smoothing Method on Forecasting Electricity Production of Australia an Empirical Study\u201d. Global Journal of Research in Engineering - J: General Engineering GJRE-J Volume 16 (GJRE Volume 16 Issue J2): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
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Total Score: 109
Country: Bangladesh
Subject: Global Journal of Research in Engineering - J: General Engineering
Authors: Md. Matiur Rahman Molla, S.M. Nuruzzaman, Dr. M. Sazzad Hossain, Md.Shohel Rana (PhD/Dr. count: 1)
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Publish Date: 2016 06, Mon
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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’s additive trend and seasonality smoothing methods are considered. Applying Theil’s U-statistic as the key performance measure, the study concludes that Holtwinter’s method is more appropriate model.
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