Pronóstico de la demanda de energía eléctrica horaria en Colombia mediante redes neuronales artificiales

the electric load forecasting of a country or a determined sector is a very important task not just from the operative point of view, but from the commercial. a neural network based full-week hourly electric load forecasting model is proposed for colombia. this model uses historical information delays as well as previously identified date events which produce significant changes in the electric load patrons through the year, the model also consider a three weeks delay in the available information used in forecasts. the model was validated using real electric load data from a specific colombian region. the results were compared with an auto-regressive model (ar) and an auto-regressive model with exogenous variables (arx). the general error decay and the good approximation during the atypical time periods, which are difficult to forecast, make it a satisfying model.