Non-technical losses (NTLs) are a major problem in modern smart grids, damaging revenue and operational functionality. This study proposes an integrated IoT-edge-cloud framework to improve fraud detection, analyze electricity usage patterns, and enhance data reliability in distributed smart grids. The approach extracts multiple features from smart meter data and uses a hybrid machine learning method that combines classification and clustering. To test the system’s robustness, the study included simulated fraud scenarios in difficult circumstances. Results show that the system achieved high detection accuracy (96.4%) and an AUC of 0.98. The framework also reduced false alarms by 86% compared to traditional rule-based methods, improving consistency and productivity. It supports near-real-time operation with response times around 125 ms and is scalable for larger smart grid environments. Behavioral segmentation further improved reliability by identifying differences in electricity consumption and reducing incorrect classifications. Overall, the study shows that combining data quality management, behavioral analysis, and distributed processing yields a more reliable and resilient solution for operational smart grid systems.
F. Otosi, Celestine A. Udie, F. Faithpraise· E3S Web of Conferences· 0 citations
Nigeria faces persistent electricity shortages and increasing municipal solid waste generation, both of which negatively affect economic productivity, environmental sustainability, and urban livelihood. This study evaluates the feasibility of deploying waste-to-energy (WtE) systems as an integrated solution to simultaneously address energy deficits and waste management challenges in Nigeria. A documentary-based assessment was conducted using peer-reviewed literature, national policy documents, and industry reports published between 2015 and 2026. The study applied a multi-criteria readiness framework to classify Nigerian states into high, medium, and low-readiness deployment tiers based on waste generation volume, collection efficiency, and energy recovery potential. Findings reveal different readiness levels, the highest demonstrating recoverable energy potential exceeding 50 MW. A second tier of states shows moderate feasibility but depends on improvements in waste logistics, feedstock consistency, and infrastructure efficiency. Lower-tier states remain constrained by poor waste collection systems, weak infrastructure, and policy limitations. The study further highlights that successful WtE implementation requires integrated system design, emissions control, regulatory stability, and stakeholder coordination. It recommends a phased “flagship-first” deployment strategy through public-private partnerships, complemented by an “organics-first” approach that integrates anaerobic digestion and the informal waste sector for sustainable recovery.
C. N. Onwusi, Michael Chukwujekwu, F. Faithpraise et al.· E3S Web of Conferences· 0 citations