Appling Autoregressive, Fractionally-Integrated, Moving Average Models of ARFIMA "p, d, q" Order for Daily Minimum Electric Load at West Tripoli Electricity Station in Libya
| المصدر: | المجلة العربية للنشر العلمي |
|---|---|
| الناشر: | مركز البحث وتطوير الموارد البشرية - رماح |
| المؤلف الرئيسي: | Enaami, Maryouma E. (Author) |
| مؤلفين آخرين: | Khaga, Rida M. (Co-Author) , Almahmodi, Mustafa A. (Co-Author) , Taweab, Fauzia A. (Co-Author) |
| المجلد/العدد: | ع11 |
| محكمة: | نعم |
| الدولة: |
الأردن |
| التاريخ الميلادي: |
2019
|
| الشهر: | سبتمبر |
| الصفحات: | 144 - 156 |
| ISSN: |
2663-5798 |
| رقم MD: | 1436334 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EduSearch, HumanIndex |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Time Series | ARFIMA | Short-Range Dependence "SRD" | Long-Range Dependence "LRD"
|
| رابط المحتوى: |
| المستخلص: |
In this paper, we report on our investigation of the long memory of the Daily Minimum Electric Load (DMEL) at West Tripoli Electricity Station as recorded by the Electricity Company in Libya. We fitted an Autoregressive, Fractionally-Integrated, Moving-Average (ARFIMA) Model to the measured loads using 361 daily records covering the period of almost one year, extending from 5 January 2008 to 31 December 2008. The results show that the time series is of the long memory type and that it can become stationary with fractional differencing. After performing fractional differencing and determining the number of lags of the autoregressive and moving average (ARMA) components, the long memory ARFIMA (3, 0.499, 3) model was used to fit the data. Even though this model fit the data well, its forecasts were infected by the swing in the data. We estimated the parameters of the model and used those estimates to forecast 20 out-of-sample data points. In light of the forecasting results of the model, we concluded that the ARFIMA is a great model in this regard. |
|---|---|
| ISSN: |
2663-5798 |
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