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<p>Accurate short-term electricity load forecasting is critical for ensuring reliable power system operation, particularly in the face of increasing grid complexity and the nonlinear, time-varying nature of load data. This study proposes a novel hybrid model that integrates Crested Porcupine Optimization (CPO), Particle Swarm Optimization (PSO), and Least Squares Support Vector Machine (LSSVM). Initially, CPO is employed to perform global hyperparameter optimization, effectively exploring the parameter space and preventing premature convergence. Subsequently, PSO performs local refinement of the parameters to enhance the model’s predictive accuracy and generalization capability. Finally, the optimized LSSVM model captures complex nonlinear relationships between load and external factors such as temperature, date, and holidays. To evaluate the effectiveness of the proposed CPO-PSO-LSSVM model, comparative experiments were conducted against baseline LSSVM, PSO-LSSVM, and CPO-LSSVM models using two real-world datasets. On the Jiangsu dataset (hourly resolution), the proposed model achieved reductions in MAE and RMSE of 40.2% and 50.3%, respectively, compared to LSSVM, while improving NSE from 0.604 to 0.902. On the Australian dataset (half-hourly resolution), it achieved reductions in MAE and RMSE of 25.3% and 50.8%, with an NSE increase from 0.985 to 0.996. These results confirm that the CPO-PSO-LSSVM model offers superior forecasting accuracy, robustness across regions and time granularities, and practical applicability for modern power system management.</p>

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Short-term load forecasting using a two-stage CPO-PSO hyperparameter optimization of LSSVM

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  • 发表日期：

    2026年6月18日

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  • XinHao Zhang

摘要

Accurate short-term electricity load forecasting is critical for ensuring reliable power system operation, particularly in the face of increasing grid complexity and the nonlinear, time-varying nature of load data. This study proposes a novel hybrid model that integrates Crested Porcupine Optimization (CPO), Particle Swarm Optimization (PSO), and Least Squares Support Vector Machine (LSSVM). Initially, CPO is employed to perform global hyperparameter optimization, effectively exploring the parameter space and preventing premature convergence. Subsequently, PSO performs local refinement of the parameters to enhance the model’s predictive accuracy and generalization capability. Finally, the optimized LSSVM model captures complex nonlinear relationships between load and external factors such as temperature, date, and holidays. To evaluate the effectiveness of the proposed CPO-PSO-LSSVM model, comparative experiments were conducted against baseline LSSVM, PSO-LSSVM, and CPO-LSSVM models using two real-world datasets. On the Jiangsu dataset (hourly resolution), the proposed model achieved reductions in MAE and RMSE of 40.2% and 50.3%, respectively, compared to LSSVM, while improving NSE from 0.604 to 0.902. On the Australian dataset (half-hourly resolution), it achieved reductions in MAE and RMSE of 25.3% and 50.8%, with an NSE increase from 0.985 to 0.996. These results confirm that the CPO-PSO-LSSVM model offers superior forecasting accuracy, robustness across regions and time granularities, and practical applicability for modern power system management.