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.

提出了一种混合模型，结合了卷积神经网络和堆叠双向长短期记忆架构，以准确预测达卡市电力需求的短期预测，该方法在比较中表现最佳，优于其他基准模型，并且对于负载预测也超过了现有的某些研究成果。

使用堆叠的双向 LSTM 和 CNN 对达卡市短期电力需求进行预测

Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with  Stacked BiLSTM

The accurate wind speed series forecast is very pivotal to security of grid
dispatching and the application of wind power. Nevertheless, on account of
their nonlinear and non-stationary nature, their short-term forecast is
extremely challenging. Therefore, this dissertation raises one short-term wind
speed forecast pattern on the foundation of attention with an improved gated
recurrent neural network (AtGRU) and a tactic of error correction. That model
uses the AtGRU model as the preliminary predictor and the GRU model as the
error corrector. At the beginning, SSA (singular spectrum analysis) is employed
in previous wind speed series for lessening the noise. Subsequently, historical
wind speed series is going to be used for the predictor training. During this
process, the prediction can have certain errors. The sequence of these errors
processed by variational modal decomposition (VMD) is used to train the
corrector of error. The eventual forecast consequence is just the sum of
predictor forecast and error corrector. The proposed SSA-AtGRU-VMD-GRU model
outperforms the compared models in three case studies on Woodburn, St. Thomas,
and Santa Cruz. It is indicated that the model evidently enhances the
correction of the wind speed forecast.

基于改进的门控循环神经网络（AtGRU）和错误校正策略，本论文提出了一种短期风速预测模型。该模型通过使用 AtGRU 模型作为初步预测器和 GRU 模型作为错误纠正器，利用 Singular Spectrum Analysis (SSA) 对风速序列降噪，并通过 Variational Modal Decomposition (VMD) 对预测误差进行训练。在三个案例研究中，该模型在 Woodburn、St. Thomas 和 Santa Cruz 的风速预测中表现出色，明显提高了风速预测的准确性。