## 1 Introduction
To address these challenges, a novel sequence-to-sequence hybrid model was introduced in [11], which combines the CNN with LSTM networks to forecast monthly peak loads over a three-year horizon. By focusing on the monthly peak load, the model avoided unnecessary complications while still providing all the essential information needed for effective long-term strategic planning. The accuracy of the proposed method was validated using load data from New South Wales (NSW), Australia, with numerical results demonstrating superior prediction accuracy compared to existing long-term load forecasting models. To further improve the prediction accuracy of electricity demand, some other strategies have been employed recently, such as a combination of various machine learning models and using the latest deep learning models with an attention mechanism. In [12], the authors utilized a CNN-LSTM model with an attention mechanism to forecast electricity demand within a smart grid environment. Their findings indicated that the attention-enhanced model outperformed conventional CNN-LSTM models, achieving a reduced MAPE and Mean Absolute Error (MAE). Reference [13] utilized a CNN-LSTM model with an attention mechanism to predict day-ahead electricity demand in a renewable energy-heavy grid. The model combined historical demand data with weather factors such as temperature, wind speed, and solar radiation. Their results demonstrated that this approach can significantly improve the forecasting performance compared to conventional models, making it particularly useful for managing the variability and uncertainty inherent in renewable energy sources. In the study of [14], the author developed an enhanced sequence-to-sequence GRU model capable of capturing dynamic temporal dependency patterns in the data. This model used calendar, weather, and historical load data to achieve its predictions. The author of [15] applied the Temporal Fusion Transformer (TFT) model for electricity demand forecasting. This model leverages self-attention mechanisms to capture the relationships between various elements in a sequence. Despite the advancements in electricity demand forecasting, existing models still face challenges in capturing the complex and dynamic nature of electricity consumption, especially in the context of varying climatic conditions. Most conventional models, including those utilizing machine learning and regression-based approaches, often struggle to adequately account for the nonlinear relationships between multiple influencing factors such as temperature fluctuations, economic activity, and energy usage patterns over time. Moreover, while some studies have incorporated weather variables like the HDD and CDD, there is still a lack of integration of advanced techniques that can enhance prediction accuracy by focusing on the most relevant data points. This research addresses these issues by introducing a hybrid CNN-LSTM model enhanced with an attention mechanism. The proposed model distinctly utilizes HDD and CDD data to effectively capture the significant impact of temperature fluctuations on energy demand. It improves the precision of day-ahead electricity demand forecasting. It is a reliable energy management tool, particularly in regions such as NSW, Australia, where climate variability greatly influences energy consumption patterns. The rest of this paper is organized as follows: Section 2 presents the proposed prediction model. Section 3 demonstrates the prediction results of various models, including the proposed model, and discusses the results. Finally, Section 4 concludes this paper.