Deep Learning Based Load Forecasting for Futuristic Sustainable Smart Grid

Power utilization has expanded dramatically during the previous few decades. This expansion is intensely troubling the power merchants. Subsequently, anticipating the future interest for power utilization will give an advantage to the power wholesaler. Anticipating power utilization requires numerous boundaries. This work presents two approaches with one using a deep learning based Long Short-Term Memory(LSTM) and Gated Recurrent Unit for short term load forecast These models consider the previous electricity consumption to predict the future electricity consumption. The data for modelling is taken from London Smart Energy Meter dataset. The performance of the models was evaluated against the root mean sqaure error to check the best method that can be utilized in load forcasting.