## 2 The Proposed Prediction Method  
The proposed prediction method was divided into four main steps: data collection, pre-processing of the data, constructing benchmark DL models, and building the CNN-LSTM-Attention model. Step 1: Data Collection  
The dataset used in this study was aggregated by combining the energy data from AEMO's dataset with the weather data from the Sydney Bureau of Meteorology. This dataset contains 8640 records per year, which includes hourly electricity demand (MW) and hourly weather parameters. The weather data that were utilized to build the benchmark and the proposed electricity demand prediction models include the temperature (\({}^{\circ}\)C), humidity (\(\%\)), wind speed (km/h), CDD (Degree-days), and HDD (Degree-days). The dataset range utilized for the forecasting model in this study spans from January 2020 to December 2020. In addition, the models were evaluated based on the data from the years 2022 and 2023. CDD and HDD are critical indices that quantify the impact of temperature on energy consumption. The degree day is a numerical index initially developed by engineers specializing in heating, ventilation, and air conditioning [16]. The HDD index assumes that people will start using their furnaces when the average daily temperature falls below a specific threshold. In NSW, the heating critical temperature (CT) was set to 17 \({}^{\circ}\)C by the Australian National Electricity Market (NEM) [17]. The CDD index evaluates the energy demands necessary to cool buildings and residences. When the average daily temperature exceeds a specific threshold, individuals will start using measures such as air conditioning to cool their surroundings. In NSW, the cooling critical temperature was set to 19.5 \({}^{\circ}\)C by NEM [17]. By incorporating CDD and HDD into our hybrid CNN-LSTM model, we can accurately model the temperature sensitivity of energy demand. This approach is particularly valuable for forecasting in climates like NSW, where seasonal temperature changes lead to substantial fluctuations in energy usage. Step 2: Data Pre-Processing  
The electricity demand dataset comprises 8760 hourly entries spanning 1 January 2020, to 31 December 2020, with features including DEMAND, temperature, wind speed, humidity, heat index, CDD, and HDD. This dataset was used to train a CNN-LSTM-Attention model for electricity demand forecasting. Given the model's reliance on sequential input features and its sensitivity to temporal dependencies and feature scales, several preprocessing steps were applied to optimize the data for analysis. An initial assessment identified a minor proportion of missing values within the dataset, primarily in the wind speed and humidity features, accounting for approximately 1.5% of the total entries. To address this, we imputed missing values using the mean value of the respective feature, calculated across the entire dataset. To ensure consistent input scales for the CNN and LSTM layers of the CNN-LSTM-Attention model, we standardized the numerical features using the standardization formula:  
\[X_{stand}=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(X_{stand}\) is the standardized variable, \(x\) is the actual value, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. Standardization was implemented using the StandardScaler from the scikit-learn library, with parameters fitted on the training set and applied to both training and test sets to prevent data leakage. After standardization, all features had a mean of 0 and a standard deviation of 1, which facilitated faster convergence during training and improved the model's ability to learn temporal patterns. The target variable (DEMAND) was similarly standardized to align with the model's input-output scale, with an inverse transformation applied to obtain predictions in the original MW scale. This preprocessing step notably improved prediction accuracy, as evidenced by a reduction in the Mean Absolute Percentage Error (MAPE) during model training.