Strategy Improved Pelican Algorithm Optimization ELM for Short-Term Electricity Load Forecasting

When applying extreme learning machine (ELM) to short-term power load forecasting, its randomized weights and thresholds result in relatively low prediction accuracy and stability. Meanwhile, the pelican optimization algorithm (POA) suffers from the limitation of easily falling into local optima. To address these issues, this study proposes an improved pelican optimization algorithm (IPOA) to optimize ELM for short-term power load forecasting. The proposed method first incorporates an improved one-dimensional chaotic mapping (1-SCEC), Levy flight strategy, and adaptive weight strategy to enhance the optimization capability of POA, with the superiority of IPOA validated through two standard test functions. Subsequently, IPOA is employed to optimize ELM parameters, establishing an IPOA-ELM-based short-term power load forecasting model. The feasibility of the IPOA-ELM model is verified using actual power load forecasting data from an Australian region. Experimental results demonstrate that the proposed model achieves closer prediction results to actual loads for both weekends and weekdays, exhibiting superior prediction accuracy and stability compared to alternative methods.