Hybrid Input Data Selection Technique for Neural Network Based Load Forecast Model

Abstract

Selection of best model parameters is a key to building an effective neural network based prediction model. This paper focuses on short term load forecasting (STLF) framework emphasizing on a new hybrid data selection preprocessing and selection technique to enhance forecast accuracy. A Multilayer perceptron neural network (MLPNN) is used to predict the load demand of one week ahead. Because of the mapping and memorizing ability of the artificial neural network (ANN) towards the non-linear relations between input and output variables, they are widely used in predictive modeling, such as, electrical load demand prediction models. The non-linear behaviour of the load can only be narrated effectively by ANN, if a group of appropriate inputs is identified prior to the training process. A new input variable selection method based on statistical methods and evolutionary computation technique genetic algorithm (GA) is proposed in this research. A case study is developed to verify the efficacy of the optimized training dataset and input variable selection method. In this approach, we trained the ANN with conventional back propagation (BP) training algorithm with and without using the optimized and reduced dataset. Results show that this dataset selection method is effective and forecasts electrical load demand with reduced mean absolute percentage error (MAPE) and reduced training time.