<![CDATA[Short-term load forecasting (STLF) is critical in modern power system planning and operation. However, the effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) depends on selecting hyperparameters, which are traditionally tuned through time-consuming trial-and-error processes. The research contribution of this study is to systematically analyze how two key parameters—swarm size and iteration count—in Particle Swarm Optimization (PSO) affect the performance of CNN hyperparameter tuning for STLF. A CNN architecture with fixed convolutional depth is optimized using PSO over selected hyperparameters, including the number of filters, batch size, and training epochs. The experiments use two regional Australian electricity load datasets: New South Wales (NSW) and Queensland (QLD). A three-fold cross-validation strategy is employed, and the Mean Absolute Percentage Error (MAPE) is used as the primary evaluation metric. The results show that optimal PSO configurations vary significantly between datasets, with smaller swarm sizes and moderate iteration counts yielding favorable trade-offs between forecasting accuracy and computational cost. However, the reliance on MAPE, sensitivity to near-zero values, and fixed CNN architecture impose limitations. This study provides practical guidance for selecting PSO settings in deep learning-based STLF and demonstrates that tuning PSO configurations can significantly enhance model performance while reducing computational overhead. Future work may explore adaptive or hybrid optimization methods and extend to more diverse forecasting scenarios.]]>
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