  The precise forecasting of electricity demand also referred to as load
forecasting, is essential for both planning and managing a power system. It is
crucial for many tasks, including choosing which power units to commit to,
making plans for future power generation capacity, enhancing the power network,
and controlling electricity consumption. As Bangladesh is a developing country,
the electricity infrastructure is critical for economic growth and employment
in this country. Accurate forecasting of electricity demand is crucial for
ensuring that this country has a reliable and sustainable electricity supply to
meet the needs of its growing population and economy. The complex and nonlinear
behavior of such energy systems inhibits the creation of precise algorithms.
Within this context, this paper aims to propose a hybrid model of Convolutional
Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM)
architecture to perform an accurate short-term forecast of the electricity
demand of Dhaka city. Short-term forecasting is ordinarily done to anticipate
load for the following few hours to a few weeks. Normalization techniques have
been also investigated because of the sensitivity of these models towards the
input range. The proposed approach produced the best prediction results in
comparison to the other benchmark models (LSTM, CNN- BiLSTM and CNN-LSTM) used
in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092. The result
of the proposed model also outperformed some of the existing works on
load-forecasting.
