Streamflow forecasting by a feedforward neural network with Levenberg-Marquardt training using the MATLAB toolboxes

Abstract

Load Forecasting is very important for planning and operation of electrical power systems. Since the advance of neural networks and the application in forecasting problems, they are very interesting and the statistical traditional methods like ARIMA of Box and Jenkins become unused. Feedforward neural network with training with backpropagation is one of the most used methods found on the literature, but the convergence is not so fast. The Levenberg-Marquardt training algorithm is a very useful alternative due to the faster convergence. This work intends to use a feedforward neural network trained with Levenberg-Marquardt algorithm by the toolboxes of MATLAB applied to electrical load forecasting with data from a Brazilian electrical power company.