ARAR algorithm in forecasting electricity load demand in Malaysia

Electricity load demand has grown more than four-fold over the last 20 years period. The purpose of the current study is to evaluate the performance of ARAR model in forecasting electricity load demand in Malaysia. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) will be used as a benchm...

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Main Authors: Miswan, Nor Hamizah, Hussin, Nor Hafizah, Mohd Said, Rahaini, Hamzah, Khairum, Ahmad, Emy Zairah
格式: Article
語言:English
出版: Research India Publications 2016
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在線閱讀:http://eprints.utem.edu.my/id/eprint/16996/2/19_43000-%20GJPAM%2097%20ok%20361-367%20author%20self1.pdf
http://eprints.utem.edu.my/id/eprint/16996/
http://www.ripublication.com/gjpam16/gjpamv12n1_32.pdf
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總結:Electricity load demand has grown more than four-fold over the last 20 years period. The purpose of the current study is to evaluate the performance of ARAR model in forecasting electricity load demand in Malaysia. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) will be used as a benchmark model since the model has been proven in many forecasting context. Using Root Mean Square Error (RMSE) as the forecasting performance measure, the study concludes that ARAR is more appropriate model.