Artificial Intelligence for Predictive Analytics in Nigeria’s Energy Sector: A Review, Use-Case Map, and Implementation Roadmap
Arionget Jemima
Department of Pharmacoepidemeology Kampala International University Uganda
Email: jemima.arionget@studwc.kiu.ac.ug
ABSTRACT
Nigeria’s energy sector faces persistent challenges: demand–supply imbalance, grid instability, high technical/commercial losses, fuel supply constraints, and operational downtime across electricity and hydrocarbons. Predictive analytics (forecasting, anomaly detection, predictive maintenance, and risk prediction) powered by Artificial Intelligence (AI) can materially improve planning, reliability, revenue assurance, and asset performance. This review synthesizes the main AI methods used for predictive analytics, maps high-value use cases across Nigeria’s electricity and oil & gas value chains, and proposes a practical implementation roadmap tailored to local data realities (e.g., metering gaps, limited SCADA coverage, fragmented systems). Evidence from Nigeria-focused studies and sector reports indicates strong opportunity in (i) load and renewable generation forecasting, (ii) outage/fault prediction and grid event early warning using SCADA/EMS and feeder data, (iii) ATC&C loss and theft analytics leveraging meter/billing/feeder reconciliation, and (iv) predictive maintenance for rotating equipment, pipelines, and processing facilities. Key barriers are data availability/quality, interoperability, cybersecurity, skills, and governance. The paper concludes with a phased strategy emphasizing data readiness, prioritized pilots, MLOps, regulatory alignment, and responsible AI.
Keywords: Nigeria, predictive analytics, AI, machine learning, load forecasting, predictive maintenance.
CITE AS: Arionget Jemima (2026). Artificial Intelligence for Predictive Analytics in Nigeria’s Energy Sector: A Review, Use-Case Map, and Implementation Roadmap. IDOSR JOURNAL OF SCIENCE AND TECHNOLOGY 12(2):58-62, 2026. https://doi.org/10.59298/IDOSR/JST/26/122.5862