Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting

Electricity inspection is important to support sustainable development and core the marketing of electric power. In addition, it contributes effective management power companies their financial performance. Continuous improvement in penetration rate new energy generation can improve environmental standards promote development, but creates challenges for electricity inspection. Traditional methods are time-consuming quite inefficient, which hinders firms. this paper, a load-forecasting model based on an improved moth-flame-algorithm-optimized extreme learning machine (IMFO-ELM) proposed use A chaotic map linear decreasing weight introduced convergence ability traditional moth-flame algorithm obtain optimal parameters ELM. Abnormal data points screened out determine causes abnormal occurrences by analyzing prediction results user’s actual consumption. The show that, compared with existing PSO-ELM MFO-ELM models, root mean square error reduced at least 1.92% under same conditions, supports application IMFO-ELM power-load-forecasting-based detection method efficiency inspection, enhance user experience, contribute intelligence level firms development.