The Application of Committee Machine Model in Power Load Forecasting for the Western Region of Saudi Arabia

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.