Design and Performance Analysis of a Solar-Powered Smart Energy Management System for Rural Electrification in Nigeria

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@article{1719768,
      author = {Ibrahim Musa Ibrahim, Ali Ahmad Bukar, Mohammed Mustapha},
      title = {Design and Performance Analysis of a Solar-Powered Smart Energy Management System for Rural Electrification in Nigeria},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {2198-2211},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719768.pdf},
      abstract = {Nigeria's rural areas continue to experience severe electricity access deficits, with rural grid penetration below 30 percent and average urban supply availability of only six to seven hours daily. This paper examines the design architecture, control logic, and performance characteristics of a solar-powered smart energy management system (SEMS) developed to address this gap through decentralised, intelligence-driven electrification. Evidence from the photovoltaic engineering, microgrid control, Internet of Things (IoT), and artificial intelligence literature is synthesised to propose an integrated architecture comprising a photovoltaic generation subsystem, hybrid lithium-ion/lead-acid battery storage, a microcontroller-based smart energy management unit, IoT-enabled remote monitoring, and machine-learning-based load forecasting and demand-side management. Maximum power point tracking (MPPT) strategies, charge controller topologies, and communication protocols suited to low-bandwidth rural environments are critically analysed. A techno-economic and reliability framework, informed by HOMER-based case studies from comparable Nigerian rural contexts, evaluates system sizing, levelised cost of electricity, loss-of-power probability, and renewable energy fraction. Hybrid fuzzy-logic-enhanced MPPT controllers combined with predictive load scheduling can improve system efficiency by 12 to 25 percent relative to conventional fixed-threshold control, while IoT-based demand-side management can reduce peak load by 40 to 76 percent through adaptive appliance prioritisation. Barriers to large-scale deployment, including financing gaps, technical capacity, quality assurance, and policy inconsistency, are evaluated, and a replicable framework for scaling smart solar microgrids across Nigeria's agro-climatic zones is proposed.},
      keywords = {Artificial Intelligence, Internet of Things, Microgrid, Nigeria, Rural Electrification, Smart Energy Management, Solar Photovoltaic Systems.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719768}
  }