## 3 Performance Results
### Proposed ML Model Results  
K-fold cross-validation with \(K=10\)[4] was used with the proposed ML model to mitigate overfitting and ensure robust solutions. Table 3 gives the validation results for load, costs, and carbon emissions. This shows that the model lowers the annual load to 17,000 kWh/year from the base of 20,000 kWh/year which is a 15% reduction. The annual costs are 1700 CAD/year which is 15% less than the base of 2000 CAD/year. Carbon emissions are also reduced by 15%, from 5000 kg CO\({}_{2}\)/year to 4250 kg CO\({}_{2}\)/year. These results fall within the expected range of 10-20% and thus validate the effectiveness of the proposed model in load optimization while minimizing costs and environmental impact.  
Table 4 compares the results for the proposed model and the approaches in [9; 10]. This shows that the proposed model achieves a 15% reduction in annual load, costs, and carbon emissions while the reductions with the other methods range from 8-12%. Thus, the hybrid ML model provides better adaptability and efficiency compared to traditional methods.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Predicted** & **Actual** & **Expected Range** \\ \hline Load (kWh/year) & 17,000 (15\% reduction) & 20,000 ([7;8]) & 10–20\% reduction \\ Cost Savings (CAD/year) & 1700 (15\% savings) & 2000 ([8]) & 10–20\% reduction \\ Carbon Emissions (kg CO\({}_{2}\)/year) & 4250 (15\% reduction) & 5000 ([8]) & 10–20\% reduction \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Predicted and Actual Results.  
Figure 9: ML, PSO, MOPSO, and base load results over 24 h.  
The proposed framework offers benefits to both residential customers and utilities that align with modern load management goals. For customers, the framework reduces electricity costs while ensuring load efficiency without compromising occupant comfort. The optimized load scheduling maintains the necessary heating, lighting, and appliance use while minimizing unnecessary consumption. For utilities, Demand-Side Management (DSM) through optimized usage improves grid reliability and efficiency and lowers peak demand. This leads to deferred investments in generation capacity and improved resilience. The reduction in carbon emissions conforms with national and regional decarbonization policies and regulatory requirements. Programs such as carbon credits, peak shaving, and grid flexibility encourage utilities to promote customer-side load efficiency.  
Utilities often employ Time-Of-Use (TOU) pricing, demand response, and Grid-interactive Efficient Buildings (GEBs) to provide economic and environmental benefits. This can be exploited with the proposed framework to directly benefit customers while improving grid efficiency, reducing capacity constraints, and meeting regulatory targets. While the focus here was on CSGTs in Burnaby, BC, the proposed adaptive framework can be applied in any geographic location with any energy infrastructure. This flexibility lies in the integration of real-time data such as occupancy patterns, weather conditions, and dynamic energy pricing. It can be customized to reflect diverse conditions, regulations, and constraints, and supports various grid structures (grid-connected, microgrid, off-grid). The framework can also be aligned with regional energy and load policies such as Leadership in Energy and Environmental Design (LEED), Building Research Establishment Environmental Assessment Method (BREEAM), and the National Australian Built Environment Rating System (NABERS).