## 1 Introduction
### Proposed Approach Contributions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Forecast Horizon** & **Data from** & **Tools** & **Features** \\ \hline \hline \multirow{9}{*}{[PERSON] et al., 2024 [28]} & Short- to long-term & Iran and Germany & LSTM and discrete wavelet transformation & Ambient temperature, cloud cover, solar radiation, and precipitation, along with electricity load data, are used to train the model. & Ambient temperature, cloud cover, solar radiation, and precipitation, along with electricity load data, are used to train the model. & Ambient temperature, cloud cover, solar radiation, and precipitation, along with electricity load data, are used to train the model. & Ambient temperature, cloud cover, solar radiation, and precipitation, along with electricity load data, are used to train the model. & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \multirow{9}{*}{[PERSON] et al., 2024 [29]} & \multirow{9}{*}{One day ahead} & \multirow{9}{*}{One day ahead and Chios} & \multirow{9}{*}{Mullilayer perceptron, convolutional neural network, and ensemble} & Load data from the Greek islands of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. & Load data from the fields of Rhodes and Lesvos are used to train and fine-tune the three sequence-to-sequence deep-learning models to forecast load data for Chios. The results show MAPE values ranging from 5\% to 6\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._trained more effectively on relevant data patterns, particularly for weekdays and weekends, which potentially enhances forecasting accuracy. Furthermore, by defining representative regional climate conditions, weather information--an essential factor--augments the training process for improved forecasting accuracy.