Izvorni znanstveni članak
https://doi.org/10.13044/j.sdewes.d13.0649
Enhancing Reliability of Off-Grid Energy Systems through Combined Edge-Based Analytics and Predictive Maintenance Models
John Kimotho Nyambura
orcid.org/0009-0009-4839-5164
; Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Joseph Kamau
; Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Denis Mugambi Kaburu
; Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Stephen Kinyua Gitahi
; Strathmore University, Nairobi, Kenya
Sažetak
Conventional energy generating strategies, such as reactive and scheduled maintenance, often lead to increased downtime, energy waste, and inefficiencies. This study integrates edge analytics with machine learning-based predictive maintenance to boost the reliability and sustainability of off-grid energy generating systems. Using Long Short-Term Memory and regression models, the approach enables early anomaly detection and fault prediction, reducing unplanned outages and maintenance costs. A comparative analysis between standard edge analysis and integrated edge-predictive methods shows that the integrated system achieves an accuracy of 91.6%, compared to the edge analytics model with an accuracy of 86.2% effectively stabilizing short-term fluctuations, generating fewer and more stable alerts, with a coefficient of determination R² of 0.98. Results highlight that combining predictive models with edge analytics enhances reliability, supports timely interventions, and strengthens system robustness in off-grid energy generating applications.
Ključne riječi
Off-grid renewable energy systems; Edge analytics; IoT-based monitoring; Real-time analytics; Predictive maintenance
Hrčak ID:
346111
URI
Datum izdavanja:
26.5.2026.
Posjeta: 120 *