![](/images/graphics-bg.png)
Artificial neural networks approach to time series forecasting for electricity consumption in Gaza Strip
Author
Source
IUG Journal of Natural Studies
Issue
Vol. 21, Issue 2 (30 Jun. 2013), pp.1-22, 22 p.
Publisher
The Islamic University-Gaza Deanship of Research and Graduate Affairs
Publication Date
2013-06-30
Country of Publication
Palestine (Gaza Strip)
No. of Pages
22
Main Subjects
Topics
Abstract AR
في هذا البحث تم استعراض اثنين من نماذج التنبؤ القوية, الشبكات العصبية الاصطناعية (ANNs) و نماذج الانحدار الذاتي - التكاملي - المتوسط المتحرك (ARIMA). تم مناقشة استخدام طريقة الشبكات العصبية الصناعية للتنبؤ في السلاسل الزمنية و كذلك عرض مبسط لبعض النتائج النظرية ذات الصلة.
تم استخدام العديد من عمليات المحاكاة التجريبية و ذلك من أجل اختيار أفضل خوارزمية لنموذج الشبكات العصبية الصناعية.
تم مقارنة نتائج استخدام الشبكات العصبية الصناعية مع ARIMA و ذلك بتطبيقها على بيانات لاستهلاك الكهرباء في قطاع غزة في الفترة 2000-2011.
النتيجة الرئيسية للبحث هي أن استخدام نماذج الشبكات العصبية الصناعية أفضل في التنبؤ من نموذج ARIMA.
Abstract EN
This paper introduces two robust forecasting models for efficient forecasting, Artificial Neural Networks (ANNs) approach and Autoregressive Integrated Moving Average (ARIMA) models.
ANNs approach to univariate time series forecasting and relevant theoretical results are briefly discussed.
To choose the best training algorithm for the ANN model, several experimental simulations with different training algorithms are made.
We compare ANNs approach with ARIMA model on real data for electricity consumption in Gaza Strip.
The main finding is that, comparison of performance between the two proposed models reveals that ANNs outperform and preferable in selecting the most appropriate forecasting model over the ARIMA model.
American Psychological Association (APA)
Safi, Samir Khalid Husayn. 2013. Artificial neural networks approach to time series forecasting for electricity consumption in Gaza Strip. IUG Journal of Natural Studies،Vol. 21, no. 2, pp.1-22.
https://search.emarefa.net/detail/BIM-328223
Modern Language Association (MLA)
Safi, Samir Khalid Husayn. Artificial neural networks approach to time series forecasting for electricity consumption in Gaza Strip. IUG Journal of Natural Studies Vol. 21, no. 2 (2013), pp.1-22.
https://search.emarefa.net/detail/BIM-328223
American Medical Association (AMA)
Safi, Samir Khalid Husayn. Artificial neural networks approach to time series forecasting for electricity consumption in Gaza Strip. IUG Journal of Natural Studies. 2013. Vol. 21, no. 2, pp.1-22.
https://search.emarefa.net/detail/BIM-328223
Data Type
Journal Articles
Language
English
Notes
Includes appendicxes : p. 16-20
Record ID
BIM-328223