Fuzzy inference in spatial load forecasting

Forecasting electric demand and its geographical distribution is a prerequisite to generate expansion planning scenarios for distribution planning. This paper presents a comprehensive methodology that uses a fuzzy inference model over a GIS support, to capture the behavior of influence factors on load growth patterns and map the potential for development. The load growth is spread over maps with cellular automata. The interaction with a scenario generator inputs data into a graph generator, which will serve as a basis for more classic network planning tools.