Computational intelligence approach to load forecasting - a practical application for the desert of Saudi Arabia

This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research paper two steps have been introduced, first load forecasting made by mapping mechanism and then optimization technique applied to improve its accuracy. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is an effective mathematical tool for mapping complex relationships. It is also successful for doing forecasting, categorization, classification, and so forth. On the other hand, PSO is the most promising optimization tool. It has better information sharing and conveying mechanism; it has better balance of local and global searching abilities; and can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization model of the weight matrix of ANN. Results show that the proposed ANN-PSO performs much better than ANN for the load forecasting in a desert like Saudi Arabia.