A Cellular Automaton Approach to Spatial Electric Load Forecasting

Abstract

A method for spatial electric load forecasting using a reduced set of data is presented. The method uses a cellular automata model for the spatiotemporal allocation of new loads in the service zone. The density of electrical load for each of the major consumer classes in each cell is used as the current state, and a series of update rules are established to simulate S-growth behavior and the complementarity among classes. The most important features of this method are good performance, few data and the simplicity of the algorithm, allowing for future scalability. The approach is tested in a real system from a mid-size city showing good performance. Results are presented in future preference maps.