Statistical Assessment of Commercial Electric Service Quality: A Case Study of ENEL Distribution Company in Peru

Damas-Niño M.; Arellanos-Tafur E. · Proceedings of 2025 International Conference on Artificial Intelligence, Embedded Systems, and Renewable Energy, AIESRE 2025 (2025) · DOI: 10.1109/aiesre67541.2025.11470046

Resumen

This study presents a comprehensive statistical assessment framework for evaluating commercial electric service quality within ENEL's distribution network across Peru. The statistical methodology encompasses rigorous sampling techniques, compliance rate calculations, correlation analyses, and performance benchmarking against National Technical Standards for Electric Service (NTCSE). From an initial population of 812 users, stratified sampling yielded 342 statistically representative participants across industrial, commercial, and residential segments throughout Lima. Statistical evaluation employed SPSS software for descriptive statistics, variance analysis, hypothesis testing, and regression modeling to assess service quality dimensions. The assessment revealed statistically significant compliance rates of 94.8% for customer treatment (p<0.001), 98.1% for infrastructure performance (p<0.001), and 96.3% for measurement precision (p<0.001). Customer satisfaction correlation analysis demonstrated strong positive relationships (r=0.73) with operational compliance metrics. Advanced statistical modeling including machine learning algorithms achieved 89.3% prediction accuracy for service quality patterns. This statistical framework establishes quantitative benchmarks for utility performance evaluation, providing stakeholders with empirically validated metrics that demonstrate measurable relationships between operational compliance and customer satisfaction in developing economy contexts.

Instituciones: Universidad Continental; Universidad Nacional del Callao