Document Details

Document Type : Article In Journal 
Document Title :
Intelligent Modelling Techniques of Power Load Forecasting for the Western Area of Saudi Arabia
النمذجة الذكية والتقنية الأفضل لدراسة التنبؤ بالأحمال في المنطقة الغربية بالمملكة العربية السعودية
 
Document Language : English 
Abstract : Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This paper presents the development of an ANN-based short-term load forecasting model with improved accuracy for the Regional Power Control Centre of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data, special events indexes and historical electric load-related data using the data from the calendar years 2003, to 2007 for training. Different neural networks topologies have been trained and tested for achieve the optimal topology and ranking the input variables in terms of their importance. Based on the optimal NN topology, the network has been trained to predict the ahead load at different time intervals. 
ISSN : 1319-1047 
Journal Name : Engineering Sciences Journal 
Volume : 21 
Issue Number : 1 
Publishing Year : 1431 AH
2010 AD 
Article Type : Article 
Added Date : Tuesday, March 2, 2010 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
عبدالعزيز محمد جلال الشريفAl-Shareef, AbdulAziz J.ResearcherDoctorate 
ميسم فاضل عبودAbbod, M. F.ResearcherMaster 

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