Artificial neural network for dynamic iterative forecasting : forecasting hourly electricity demand

This paper presents the procedure of building a dynamic predictive model using an artificial neural network to perform an iterative forecast. An algorithm is proposed and named as “Artificial Neural Network Approach for Dynamic Iterative Forecasting”. The development of this algorithm focused on feature selection, identification of best network architecture for the model, moving window selection and finally the iterative prediction. This proposed algorithm was deployed to forecast next day’s hourly total demand in Sri Lanka as an illustration. Inclusion of a clustering effect that were based on the specialty of the day, as an input was investigated through this application, from which improved accuracies were shown