## 3 Performance Results
Figure 2: Load profiles for the 1-bedroom SGT over 24 h.  
Figure 4: Load profiles for the 3-bedroom SGT over 24 h.  
Figure 5: Load profiles for the 4-bedroom SGT over 24 h.  
\[L_{\text{normalized},i,j}(t)=\frac{L_{i,j}(t)-L_{\text{min},i,j}}{L_{\text{max },i,j}-L_{\text{min},i,j}}, \tag{17}\]0 Load profiles for the 2-bedroom SGT over 24 h.  
Figure 7 gives the base (actual) and MOPSO optimized carbon emissions over time. This shows a 20% reduction in carbon emissions, with the peak lowered to 5.7 kg CO\({}_{2}\) from 6.3 kg CO\({}_{2}\) and a minimum of 3.8 kg CO\({}_{2}\). Figure 8 gives the base and MOPSO optimized operational costs over time. The MOPSO results range from less than 1.1 Canadian Dollar (CAD) to 1.6 CAD versus a maximum of 1.9 CAD in the base case, which indicates a cost savings of 15-20%. Figure 9 presents the load optimization results for CSGTs over 24 h considering electricity, gas, and water loads. This shows that the unoptimized (base) aggregate total load is 14.2 kWh at hour 1 and varies throughout the day with a peak of 16.8 kWh at hour 16 and a minimum of 12.2 kWh at hour 20. The ML, PSO, and MOPSO loads are progressively lower indicating improved load management. For example, at hour 16, the ML load is approximately 16.2 kWh, but the PSO load is about 15.9 kWh, and the MOPSO load is the lowest at approximately 15.5 kWh. Further, MOPSO consistently provides the best results over the 24 h and is 9.4-10.3% lower than the base load. These results indicate the effectiveness of the algorithms in reducing load consumption while maintaining load requirements. \[L_{\text{normalized},i,j}(t)=\frac{L_{i,j}(t)-L_{\text{min},i,j}}{L_{\text{max },i,j}-L_{\text{min},i,j}}, \tag{17}\]1 Base and MOPSO optimized operational costs over 24 h.  
\[L_{\text{normalized},i,j}(t)=\frac{L_{i,j}(t)-L_{\text{min},i,j}}{L_{\text{max },i,j}-L_{\text{min},i,j}}, \tag{17}\]2 Aggregated load profiles for the 1-4-Bedroom SGTs over 24 h.  
\[L_{\text{normalized},i,j}(t)=\frac{L_{i,j}(t)-L_{\text{min},i,j}}{L_{\text{max },i,j}-L_{\text{min},i,j}}, \tag{17}\]3 Base and MOPSO optimized carbon emissions over 24 h.  
While this paper considers CSGTs in Burnaby, BC, the proposed ML-based optimization framework can be generalized to other building types, locations, and energy systems. This flexibility stems from the ability to incorporate different occupancy profiles, energy consumption behavior, and load patterns using any data and real-world assumptions. By adjusting input parameters such as floor area, number of occupants, local energy pricing, and climate conditions, this framework can be employed for any residential configuration, including detached houses, apartment complexes, and smart communities. The adaptability of the proposed framework means it can be effective in any climate and with different energy systems. The LSTM-CNN model dynamically adjusts to variations in weather, energy prices, and demand-side fluctuations, so it can used with diverse energy policies and renewable energy integration. Furthermore, the MOPSO algorithm can be employed with any smart building configuration and various heating/cooling strategies, storage capacities, and demand-response mechanisms, so it can adapt to evolving energy management needs.