## 2 Background
### Energy Consumption Forecasting  
Energy consumption forecasting refers to predicting a particular region's future energy consumption based on historical consumption and other relevant factors [1]. Accurate energy consumption forecasting is essential for proper energy planning, pricing, and management. It plays a significant role in the transition toward a more sustainable energy future. There have been numerous studies on energy consumption forecasting, and various models have been proposed for this purpose [17]. As shown in Figure 1, in the literature, forecasting models often fall into two categories: _(i)_ Conventional Models and _(ii)_ Artificial Intelligence (AI) Models. Table 1 shows different forecasting models for commercial buildings. It shows the names of forecasting techniques, locations, and performance evaluation indexes with the best accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Forecasting Model** & **Year** & **Country** & \begin{tabular}{c} **Forecast** \\ **Horizon** \\ \end{tabular} & **Ref.** & & \begin{tabular}{c} **Accuracy** \\ **Marse** \\ \end{tabular} \\ \hline \multirow{2}{*}{1} & ANN model with external variables & & & & **MAPE** & \begin{tabular}{c} **Normalised-** \\ **RMSE** \\ \end{tabular} & **MAE** \\  & \begin{tabular}{c} (NARX) \\ \end{tabular} & 2019 & Korea & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & [18] & 1.69\% & 85.44 & \\ \hline \multirow{2}{*}{2} & Long Short-term Memory Networks & & & & **19** & 5.96\% & 7.21 & \\  & with attention (LSTM) & 2020 & USA & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & [19] & 5.96\% & 7.21 & \\ \hline \multirow{3}{*}{3} & AdaBoost.R2 & 2021 & Portugal & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & [20] & 5.34\% & \\ \hline \multirow{2}{*}{4} & Support Vector Machine (SVM) & 2022 & Ireland & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & [21] & 5.3\% & 3.82 & 11.94 kW \\ \hline \multirow{2}{*}{5} & Seq2 seq RNN & 2020 & USA & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & [22] & & 3.74 kW & \\ \hline \multirow{3}{*}{6} & Bayesian regularized (BR) (12 inputs) & 2019 & Canada & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & [23] & 1.83\% & 105.03 kW & \\ \cline{2-6}  & Levenberg Macquardt (LM) (12 inputs) & 2019 & Canada & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & 1.82\% & 104.21 kW & \\ \hline \multirow{3}{*}{7} & Hybrid convolutional neural network & & & & **24** & 0.76\% & 0.47 & 0.31 \\  & (CNN) with an LSTM autoencoder & 2020 & Korea & \begin{tabular}{c} hour \\ ahead \\ \end{tabular} & [25] & 5.33\% & 0.57 & 0.43 \\ \hline \multirow{3}{*}{9} & Seasonal & \begin{tabular}{c} Seasonal autoregressive integrated \\ moving average (SARIMAX) \\ \end{tabular} & 2020 & Korea & \
\begin{tabular}{c} day \\ ahead \\ \end{tabular} & [26] & 27.15\% & 557.6 kW & \\ \hline \multirow{2}{*}{10} & Gated Recurrent Unit (GRU) & 2022 & Spain &
\begin{tabular}{c} day \\ ahead \\ \end{tabular} & [27] & 7.86\% & 156.11 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of different forecasting models for commercial buildings. Conventional models used in energy consumption forecasting commonly include Stochastic time series (TS) models [30], regression models (RMs) [31], and gray models (GMs) [32,33]. These models typically require historical energy consumption data as input and use various statistical and mathematical techniques to make future energy consumption predictions. However, these models may not be able to capture complex nonlinear relationships and may require manual feature engineering, making them less efficient and scalable compared with AI-based models. AI-based models have become increasingly popular in the field of energy forecasting due to their ability to learn patterns and relationships in complex data [34, 35, 36]. The use of AI in energy forecasting has the potential to reduce energy costs, optimize energy production, and enhance energy security. Furthermore, LSTM models are able to capture both short-term and long-term dependencies in TS data.