This dissertation investigates the application of Artificial Neural Networks (ANNs) in load forecasting. In this work we have used real load data from the Brazilian electrical system. The dissertation is divided in four main topics: a study of the importance of load forecasting to electric power systems; the investigation of the ANN modeling to this particular problem; the development of a neuro-simulador; and the case studies. It has been made an investigation of the objectives of load forecasting to power systems. The different kinds of load forecasting have been classified according to the leading time of the prediction (short and long term). The more important variables to model electric load were also investigated. This study analyses many projects in the area of load forecasting and presents the techniques that have been traditionally used to treat the problem. The ANNs modeling to load forecasting involved a deep investigation of works that have been published. The ANNs architectures and learning algorithms more commonly used were studied. It has been verified that the Backpropagation algorithm was the more commoly applied in the problem (particularly, in the problem of short term hourly load forecasting). Based on this investigation and using the backpropagation algorithm, many Neural Networks architetures were proposed according to the desired type of forecasting. The development of the neuro-simulator has been made in C language, using SUN workstations. The software is divided in 3 modules: a load series pre-processing module, to prepare the input data; a training module to the load series behavior learning; and an execution module, in which the Neural Network will perform the predictions. The development of a friendly interface to the forecasting system execution and the portability of the system were main goals during the simulator development. The case studies involved testing the system performance for 2 cases: hourly and monthly predictions. In the first case, load data from CEMING (State of Minas Gerais) and LIGHT (State of Rio de Janeiro) has been used. In the second case load data from 32 companies of the Brazilian electrical system has been used. Monthly load forecasting is involved in a project of interest of two companies of the electric sector in Brazil: CEPEL and ELETROBRÁS. In both cases, influences of the forecasting horizon and of the period of the year in the system´s performance has been investigated. Besides, the changes in the forecasting performance according to the particular electric company were also studied. The performance evaluation has been done through the analysis of the following error figures: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Error) and Theil´s U. The ANN performance was also compared with the performance of other techniques, like Holt-Winteres and Box-Jenkins, giving better results in many cases.