Since load data exhibits weather sensitivity, weather-related factors are the most significant external factors impacting the load. However, external factors such as electric energy prices, days of the week, public holidays, economic indicators, and population information also affect the load [25]. To improve both stability and accuracy of the DL model, feature engineering techniques are implemented, providing the most influential factors that affect electrical load consumption among the multiple input features [26]. Nevertheless, mid-term load forecasting has yet to pay much attention to feature selection (FS), and few relevant studies exist in the literature [27; 28; 29]. Although deep learning has improved the black-box nature of machine learning in energy predictions, its increasing complexity has made it difficult to interpret ML algorithms. Since the operation and planning of power systems rely on power system experts' knowledge and experience, they may not trust the results and decisions obtained using ML-based algorithms. The power systems industry is heavily regulated, emphasizing reliability and clarity in system analysis and decision-making. Explainable AI (XAI), a popular topic in deep learning,enhances the comprehension and interpretation of DL-based algorithm results, contributing significantly to researchers and experts in this area. XAI techniques like LIME, Shapley additive explanation (SHAP), GRADient class activation mapping, and DL important features play a crucial role in modern research studies. LIME and SHAP, in particular, are prevalent techniques that offer the flexibility to explain any DL model, making them highly useful in energy systems forecasting. The study utilized the light gradient-boosting model to estimate electricity generation. SHAP was utilized to interpret the features' relation to input appropriate data into the ML model as per [30]. As per Reference [31], a graphical representation of the most important features as functions of time was suggested for short-term residential-level load forecasting. Additionally, a sequence-to-sequence RNN model-agnostic explainable model was employed for the same purpose. Reference [32] utilized an explainable RNN model based on LSTM to estimate household-level electricity usage in the short and medium term. The authors proposed an explanatory layer utilizing graphic interpretation on top of their forecasting model to clarify feature relevance. Reference [33] employed a deep autoencoder capable of explaining the most important input features that impact short-term forecasting outcomes for household-level consumption by controlling the latent space. Reference [34] conducted a study using decision tree (DT)-based ensemble learning models, and found that the LightGBM model outperformed the other models in predicting short-term load consumption for educational buildings. SHAP was utilized to display the variable importance and partial dependence plots. Overall, the majority of XAI studies have focused on short-term load forecasting at the building level. To the best of our knowledge, mid-term aggregated-level load forecasting studies have not yet utilized explainable machine learning approaches. (1) The goal of this study is to enhance the MTLF by utilizing specific approaches, models, and techniques based on the literature review and previous experience on the subject, as there is still room for the improvement of forecasting performance. The following list outlines the accomplishments and contributions towards this objective. The Australian aggregate load data was analyzed and normalized to ensure its integrity for processing by the model. The dataset used was examined for stationarity using the Augmented Dickey-Fuller statistical test method. (2) A comparative analysis was made in the study by developing separate load prediction models with the LSTM method, whose success has been confirmed by many studies in the field of ELF in the literature, and Bi-LSTM and attention-based LSTM networks, which are improved variations of this method.