Spatial Electric Load Forecasting Using a Local Movement Approach

• Main Authors: Carreno, E.M., Padilha-Feltrin, A.
• Format: Conference Proceeding
• Language: English
• Subjects: agent based models; Cities and towns; Data mining; distribution planning; Evolutionary computation; Information resources; knowledge extraction; land use; Legged locomotion; Load forecasting; Pattern recognition; Predictive models; Shape; Spatial electric load forecasting; Stochastic processes
• DOI: 10.1109/ISAP.2009.5352827

An agent based model for spatial electric load forecasting using a local movement approach for the spatiotemporal allocation of the new loads in the service zone is presented. The density of electrical load for each of the major consumer classes in each sub-zone is used as the current state of the agents. The spatial growth is simulated with a walking agent who starts his path in one of the activity centers of the city and goes to the limits of the city following a radial path depending on the different load levels. A series of update rules are established to simulate the S growth behavior and the complementarity between classes. The results are presented in future load density maps. The tests in a real system from a mid-size city show a high rate of success when compared with other techniques. The most important features of this methodology are the need for few data and the simplicity of the algorithm, allowing for future scalability.