Short term load forecasting using a synchronously operated recurrent neural network

A keypoint of the control of a power system is the forecast of the short term load. The paper presents a dynamic model for short term load forecasting (STLF) which uses a recurrent neural network. This network can be used to build empirical models for the load of a dynamic system. We investigate this problem applying a basic neural network with feedback connections which is unfolded in time and becomes a general feedforward network with weights sharing. The main advantage of this model consists in unfolding the network in time, which becomes a non fully connected feedforward network and facilitates the training stage. At the same time our model provides a one day ahead prediction