Neural network architectures for short-term load forecasting

Different neural network architectures are presented for short-term load forecasting. The fully connected recurrent neural network (FRNN), where all neurons are coupled to one another, is difficult to train and to converge in a short time. The diagonal recurrent neural network (DRNN) is a modified model of FRNN. It requires fewer weights than FRNN and rapid convergence has been demonstrated. A dynamic backpropagation algorithm coupled with adaptive learning rate guarantees even faster convergence. Many experiments are conducted to provide the one-day ahead load forecast, and the results are compared. The effect of temperatures and functional-link net mapping are also studied by including them as the network´s inputs. The forecasting accuracy for weekend load can be improved by using a separate weekend load model