Day Ahead Energy Consumption Forecasting Through Time-Series Neural Network

Abstract

Demand response management through appliance scheduling can effectively decrease electricity bills. Similarly, it can decrease the peak demand of both consumers and the utility grid. However, prior knowledge of the load profile of the consumer is required for effective appliance scheduling. This work developed a novel time-series forecasting model within the shallow neural network framework to predict the load curve for optimal planning of demand response by a nonlinear autoregressive network with exogenous input network. The algorithms such as Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization was applied to train the model. The results were compared with conventional seasonal autoregressive integrated moving average statistical model and long short-term memory network. The results indicated that the optimum methodology is a nonlinear autoregressive network with exogenous inputs using the Bayesian regularisation algorithm, which has the lowest MSE value in the training and testing phases of 0.0031 and 0.0029, respectively. It is practical to continue designing artificial neural networks to analyze hourly load consumption in the context of the positive outcomes acquired.