# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 3 Results and Discussions
### Training Time for AI Models  
The training time of AI models is an important factor in their development and implementation. Training time impacts the efficiency of the model development cycle, which impacts how quickly the model can be iterated through, optimized, or put into use in practice. Longer training times can result in greater computational costs, more energy used, and slower research and development. Additionally, in situations that involve re-training a model frequently or adapting in quick succession according to real-world stimuli, longer training times can greatly limit model response and practicality. Therefore, AI research is concerned with reducing training times while maintaining accuracy in their models, which provides more nimble and economical solutions. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{2}{c}{**AI Models**} & \multicolumn{1}{c}{**ANN**} & \multicolumn{1}{c}{**RF**} & \multicolumn{1}{c}{**XGBoost**} & \multicolumn{1}{c}{**RBF**} & \multicolumn{1}{c}{**Autoencoder**} & \multicolumn{1}{c}{**Decision Trees**} \\ \hline \multirow{4}{*}{**Buildings**} & MAPR = 3.62\% & MAPR = 4.37\% & MAPR = 3.4\% & MAPR = 10.6\% & MAPR = 4.95\% & MAPR = 4.01\% \\  & RMSPR = 11.43\% & RMSPR = 14.13\% & RMSPR = 16\% & RMSPR = 28\% & RMSPR = 12.48\% & RMSPR = 18.50\% \\ \multirow{4}{*}{**LUCIA Building**} & KGE = 0.9373 & KGE = 0.7501 & KGE = 0.65 & KGE = 0.58 & KGE = 0.8237 & KGE = 0.8338 \\  & NSE = 0.9267 & NSE = 0.8870 & NSE = 0.88 & NSE = 0.57 & NSE = 0.9126 & NSE = 0.8081 \\  & R\({}^{2}\) = 0.9267 & R\({}^{2}\) = 0.8870 & R\({}^{2}\) = 0.88 & R\({}^{2}\) = 0.57 & R\({}^{2}\) = 0.9126 & R\({}^{2}\) = 0.8081 \\  & MAPR = 0.32\% & MAPR = 0.71 & MAPR = 0.71\% & MAPR = 3.2\% & MAPR = 0.1\% & MAPR = 0.5\% \\  & RMSPR = 1.4\% & RMSPR = 2.9\% & RMSPR = 2.1\% & RMSPR = 7.4\% & RMSPR = 0.3 & RMSPR = 1.5\% \\ \multirow{4}{*}{**FUHEM Building**} & KGE = 0.81 & KGE = 0.6 & KGE = 0.71 & KGE = 0.35 & KGE = 0.96 & KGE = 0.8 \\  & NSE = 0.96 & NSE = 0.81 & NSE = 0.9 & NSE = 0.23 & NSE = 0.9 & NSE = 0.95 \\  & R\({}^{2}\) = 0.96 & R\({}^{2}\) = 0.81 & R\({}^{2}\) = 0.9 & R\({}^{2}\) = 0.23 & R\({}^{2}\) = 0.99 & R\({}^{2}\) = 0.95 \\  & MAPR = 1\% & MAPR = 0.7\% & MAPR = 1.0\% & MAPR = 0.1\% & MAPR = 0.1\% & MAPR = 0.1\% & MAPR = 0.3\% \\  & RMSPR = 1\% & RMSPR = 1.6\% & RMSPR = 2\% & RMSPR = 0.9\% & RMSPR = 0.3\% & RMSPR = 0.7\% \\
**EII Building** & KGE = 0.9 & KGE = 0.81 & KGE = 0.86 & KGE = 0.9 & KGE = 0.97 & KGE = 0.97 \\  & NSE = 0.94 & NSE = 0.92 & NSE = 0.77 & NSE = 0.96 & NSE = 0.99 & NSE = 0.97 \\  & R\({}^{2}\) = 0.94 & R\({}^{2}\) = 0.92 & R\({}^{2}\) = 0.77 & R\({}^{2}\) = 0.96 & R\({}^{2}\) = 0.99 & R\({}^{2}\) = 0.93 \\  & MAPR = 0.1\% & MAPR = 1.09\% & MAPR = 0.34\% & MAPR = 0.1\% & MAPR = 0.01\% & MAPR = 0.2\% \\
**EII Building** & RMSPR = 0.2\% & RMSPR = 2.5\% & RMSPR = 1.1\% & RMSPR = 0.1\% & RMSPR = 0.015\% & RMSPR = 0.8\% \\
**(Heating)** & KGE = 0.97 & KGE = 0.6 & KGE = 0.92 & KGE = 0.99 & KGE = 0.95 \\  & NSE = 0.99 & NSE = 0.74 & NSE = 0.94 & NSE = 0.99 & NSE = 1 & NSE = 0.97 \\  & R\({}^{2}\) = 0.99 & R\({}^{2}\) = 0.74 & R\({}^{2}\) = 0.94 & R\({}^{2}\) = 0.99 & R\({}^{2}\) = 1 & R\({}^{2}\) = 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: AI models with evolution matrix. Figure 7 illustrates the training times in seconds for six different AI models across four distinct building scenarios: (a) LUCIA building HVAC (nZEB), (b) FUHEM building (heating), (c) EII building (cooling), and (d) EII building (heating).