## 5 Conclusions  
Within the framework of ELF, which is an indispensable part of power system planning and management studies, many studies have been carried out for various load types regarding short-term load forecasting, and while very successful forecasting results can be obtained, the number of studies in the literature regarding medium-term and long-term load forecasting is much smaller. The accuracy of forecasting results for these time periods is relatively lower depending on the length of the time horizon to be forecasted. In this context, the main objective of this study was to improve the medium-term electric load forecasting with certain approaches, models and techniques. To this end, the aggregated load dataset and its features were first analyzed through EDA and feature engineering. Multivariate medium-term ELF was performed separately with LSTM, Bi-LSTM, and attention-based LSTM approaches, which are among the most successful models in ELF. LIME, a post hoc model agnostic approach, was applied to these models based on DL, and the effects of the features of the datasets on the prediction results obtained could be interpreted. * As part of the EDA, the stationarity of the aggregated load data was tested using the Augmented Dickey-Fuller statistical test method, and the statistical measures obtained as a result showed that the dataset was stationary. * The correlation and importance of each feature was determined using the hybrid framework developed in feature engineering. Accordingly, 'DryBulb', 'WetBulb' and 'ElecPrice' were selected as the three most important features among the previous five features of the dataset by the combined feature selection approaches. Figure 14: LIME results of Model1, Model2 and Model3 by using the Subset3 dataset. * Since this is a month-ahead load forecast, all forecast models have been trained with the time lags of all characteristics up to 30 days ago. * Comparative results of three different subsets created to study the effect of the selected features and the determined time delay values on the models are shown in Table 6 and Figure 15 below. It can be seen from the table that the RMSE and MAPE values are highest in Subset1, where all features are present, obtaining 0.074 and 0.077, respectively, while the values of these metrics decrease by more than 20% in Subset2, which is obtained by selecting only the most effective features. In Subset3, where the most effective features have been obtained by selecting the most important time lags of the features, the RMSE and MAPE results have the lowest values of 0.046 and 0.044, respectively. There is a decrease of more than 40% in the prediction error measurements compared to Subset1 with the use of Subset3. In this study, a significant improvement was achieved in terms of prediction model performance. Based on this point, it can be concluded that feature selection is effective for this study in improving model performance and prediction accuracy, and retraining by selecting important lag features is a correct strategy. Quantitative and detailed evaluations have been conducted, and experimental results show that this entire solution approach is fully successful for multivariate medium-term aggregated load forecasting. Our forward-looking studies will primarily be carried out to develop medium- and long-term load forecasting, which has not been studied much in the literature so far. In this direction, we plan to work on the development of DL approaches and ensemble DL architectures. However, we also plan to develop feature engineering studies considering the results obtained with XAI. We also considered that the time delay value for the features can be obtained with the help of optimization techniques. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Subsets** & **RMSE** & **MAPE** \\ \hline Subset1 & 0.074 & 0.077 \\ \hline Subset2 & 0.058 & 0.055 \\ \hline Subset3 & 0.046 & 0.044 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall metric results based on subsets.