  With the evolution of power systems as it is becoming more intelligent and
interactive system while increasing in flexibility with a larger penetration of
renewable energy sources, demand prediction on a short-term resolution will
inevitably become more and more crucial in designing and managing the future
grid, especially when it comes to an individual household level. Projecting the
demand for electricity for a single energy user, as opposed to the aggregated
power consumption of residential load on a wide scale, is difficult because of
a considerable number of volatile and uncertain factors. This paper proposes a
customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM)
architecture to address this challenging problem. LSTM and GRU are
comparatively newer and among the most well-adopted deep learning approaches.
The electricity consumption datasets were obtained from individual household
smart meters. The comparison shows that the LSTM model performs better for
home-level forecasting than alternative prediction techniques-GRU in this case.
To compare the NN-based models with contrast to the conventional statistical
technique-based model, ARIMA based model was also developed and benchmarked
with LSTM and GRU model outcomes in this study to show the performance of the
proposed model on the collected time series data.
