Internet of Energy: A Deep Learning Based Load Prediction

Abstract

Smart grid being the major component of our power system is progressing fast to become more flexible and interactional. For the proper operation and planning in these systems, the load forecasting is of prime concern. In spite of the fact that many traditional methods are present for load forecasting but still a prediction of electrical load is needed to be explored further as the variation from time and surrounding weather conditions make it too complex. In this paper, we present deep learning based recurrent neural network approach for the short term load prediction using data captured from smart meters. This framework helps us to handle the over-fitting problem and uncertainty issues. The Tensor flow as a deep learning platform is used for our implementation. We compared our proposed model with the autoregressive integrated moving average model (ARIMA) and Fbprophet model and it is manifested that the proposed model outperforms in terms of RMSE, MSE, and MAE.