Atmospheric pressure applied to a neural network based short term load forecasting

The electric load is strongly related to meteorological conditions and forecast models depend on climatic studies. This work studies the influence of atmospheric pressure applied to load forecast, aimed to reduce the number of data acquisition sites and the cost related to assembly, operation and maintenance of the meteorological telemetry network. An experiment was made using a time series of the load, load with temperature, load with pressure and, finally, load with temperature and pressure. All systems were based on artificial neural networks (multilayered perceptron training by backpropagation algorithm).