Load forecasting plays an essential role in power system planning. The
efficiency and reliability of the whole power system can be increased with
proper planning and organization. Residential load forecasting is
indispensable due to its increasing role in the smart grid environment.
Nowadays, smart meters can be deployed at the residential level for
collecting historical data consumption of residents. Although the
employment of smart meters ensures large data availability, the
inconsistency of load data makes it challenging and taxing to forecast
accurately. Therefore, the traditional forecasting techniques may not suffice
the purpose. However, a deep learning forecasting network-based long
short-term memory (LSTM) is proposed in this paper. The powerful
nonlinear mapping capabilities of RNN in time series make it effective along
with the higher learning capabilities of long sequences of LSTM. The
proposed method is tested and validated through available real-world data
sets. A comparison of LSTM is then made with two traditionally available
techniques, exponential smoothing and auto-regressive integrated moving
average model (ARIMA). Real data from 12 houses over three months is
used to evaluate and validate the performance of load forecasts performed
using the three mentioned techniques. LSTM model has achieved the best
results due to its higher capability of memorizing large data in time series-based predictions

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