ENHANCING END-USER MONITORING IN SMART GRID SYSTEMS THROUGH COMPUTATIONAL PIPELINING: A CASE FOR NIGERIA

Abstract

The integration of computational pipelining in smart grid systems has emerged as a critical innovation for improving real-time monitoring, anomaly detection, and end-user responsiveness. In Nigeria, the traditional electrical grid suffers from inefficiencies, unstable power distribution, and a lack of transparency in energy consumption data. This paper proposes a computationally pipelined smart grid architecture designed to enhance end-user monitoring and improve decision-making for utilities and consumers. The proposed framework leverages distributed data collection, parallel data processing, and real-time analytics to address latency and throughput challenges common in Nigeria’s power infrastructure. Using simulated datasets based on regional smart meter data and communication delays typical of Nigerian grid systems, results demonstrate that the pipelined architecture reduces data processing latency by approximately 75% compared to conventional sequential models. The findings underscore the potential of computational pipelining as a viable technical solution to improve transparency, efficiency, and scalability in Nigeria’s evolving smart grid ecosystem.