Next 24-Hours Load Forecasting Using Artificial Neural Network (ANN) for the Western Area of Saudi Arabia

Load forecasting has become in recent years one of themajor areas of research in electrical engineering. Most traditionalforecasting models and artificial intelligence neural networktechniques have been tried out in this task. Artificial neural networks(ANN) have lately received much attention, and a great number ofpapers have reported successful experiments and practical tests. Thispaper presents the development of an ANN-based short-term loadforecasting model with improved generalization technique for theRegional Power Control Center of Saudi Electricity Company,Western Operation Area (SEC-WOA). The proposed ANN is trainedwith weather-related data, special events indexes and historicalelectric load-related data using the data from the calendar years 2003,to 2007 for training. The models tested for one week at two differentseasons, typically, summer and Ramadan seasons, the mean absoluteaverage error for day-ahead load forecasting are found 1.57% and1.82% respectively.