Short Term Load Forecasting for Lattakia City Using Artificial Neural Networks (ANNs)
Abstract
يتناول بحثنا هذا جانباً محدداً من عمليات التنبؤ بالحمولات الكهربائية، وهو التنبؤ قصير الأمد بالحمولات الكهربائية (STLF-Short Term load Forecasting)، وذلك باقتراح منظومة تنبؤ قصير الأمد للطلب على الحمل الكهربائي باستخدام الذكاء الصنعي، فقد جرى التركيز في عملية التنبؤ على استخدام الشبكات العصبية الصنعية (ANN-Artificial neural network) التي تبني خوارزميتها المعقدة؛ لترفع مستوى الأداء للمنظومة.
استناداً إلى المنظومة المقترحة صُمِمَ برنامج بلغة "MATLAB"؛ بغية محاكاة عملها، وقمنا بالتيقّن من عمل هذا البرنامج اعتماداً على معطيات ذروة الحمل الساعي للطاقة الكهربائية المستهلكة في مدينة اللاذقية من تاريخ 1-6-2010 حتى تاريخ 1-7-2010 التي حصلنا عليها من المؤسسة العامة لاستثمار الطاقة الكهربائية، ونقلها، التابعة لوزارة الكهرباء في الجمهورية العربية السورية، وتبينّ من خلال النتائج التي حصلنا عليها أنّ البرنامج ذو كفاءة عالية جداً، ويصلح لتقدير الطلب بدقة حسابية عالية، ومقبولة في التطبيقات.
This paper deals with certain aspect of electrical loads forecasting process which is the short term load forecasting (STLF) through proposing a short term forecasting system for the electrical load demand by employing the artificial neural networks which build its algorithm to boost the performance and to increase the noise immunity of the system.
Based on the proposed system a (MATLAB) language- software has been designed with the objective of simulating its function, we made sure of effective function of this software based on the hourly load apex data of electrical energy consumed in Lattakia city from the date of 01/6/2010 till the date of 01/7/2010 which was obtained from the general organization of electrical power, a subsidiary of the ministry of electricity in Syria. The results we concluded revealed that the software is highly efficient and capable to assess the load demand in a very high and acceptable mathematical accuracy in practice.
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