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GUIRELLI, C. R. Short Term Load Forecasting in Electrical Areas Using Artificial Intelligence. 2006. 127p. Thesis (Doctor Degree) – Escola Politecnica, Universidade de Sao Paulo, Sao Paulo, 2005. Nowadays, with privatization of utility companies and increase in competition in the energy market, companies must increase their service quality and ensure profits. Short term load forecasting is essential for operation of power systems and can increases security and reduces generation costs. Forecasting the load demands the identification of load patterns and its relations with exogenous variables such as weather. Originally, the problem was solved using mathematics and statistics with techniques such as time series, which produces good results but are complex and have a difficult modeling. With the advent of artificial intelligence techniques, new tools capable of dealing with large amounts of data and learn by themselves system variables relations were available. Artificial neural networks and fuzzy logic came up as the most suitable for load forecasting that have been tested and used for load forecasting for the last 20 years. This work presents a methodology for daily load forecasting of electrical areas using artificial intelligence techniques, specifically neural networks. At first, forecasting techniques are presented with emphasis on neural networks and fuzzy logic. Acquisition and treatment of data are analyzed. The load forecasting using neural networks and fuzzy logic is described and the results of the development and tests of a load forecasting system at CTEEP Transmissao Paulista presented. As contribution of this thesis, Wavelet transform is analyzed as a tool for denoising and data compression for neural network load forecasting.

Previsão da carga de curto prazo de áreas elétricas através de técnicas de inteligência artificial.