Document Details

Document Type : Article In Journal 
Document Title :
The Application of Committee Machine Model in Power Load Forecasting for the Western Region of Saudi Arabia
نموذج المكائن المتعددة لشبكة العصبية الاصطناعية للتنبؤ بالأحمال الكهربائية للمنطقة الغربية – المملكة العربية السعودية
 
Document Language : Arabic 
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 techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and many papers have reported successful experiments and practical tests. This paper presents the development of an ANN-based committee machine load forecasting model with improved accuracy for the Regional Power Control Centre of Saudi Electricity Company. The proposed system has been further optimized using Particle Swarm Optimization (PSO) and Bacterial Foraging (BG) optimization algorithms. Results were compared for standard ANN, weight optimized ANN, and ANN committee machine models. The networks were trained with weather-related, time based and special events indexes for electric load data from the calendar years 2005 to 2007. 
ISSN : 1319-1047 
Journal Name : Engineering Sciences Journal 
Volume : 22 
Issue Number : 1 
Publishing Year : 1432 AH
2011 AD 
Article Type : Article 
Added Date : Sunday, December 12, 2010 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
عبد العزيز محمد الشريفAl-Shareef, A MResearcher  
ميسم عبودAbbod, M Researcher  

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