Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access

Design and Implementation of a Smart Energy Management System for Solar-Powered Buildings Using Predictive Energy Control: A Case Study of University of Delta, Agbor, Delta State

Obonyano Kingdom Nelson, Onyemelife Ezeh Solomon, Joseph Mene, Ihieabiaobini Sylvester

8 September 2026 · Vol. 2, Issue 9, pp. 483-494

DOI: 10.65150/EP-gjetr/V2E9/2026-08

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Abstract

A Smart Energy Management System (SEMS) was conceptualized, constructed, and tested for solar-powered institutions at the University of Delta, Agbor, Delta State, Nigeria. The system included solar power, lithium-ion batteries, Internet of Things (IoT)-based monitoring, machine-learning-based demand forecasting and intelligent load management with automatic switching components to enhance energy availability and utilization. The study adopted a quantitative comparative research design using a prototype research approach. Twenty sampled buildings were selected for the experiment conducted over a year (June 2025 to May 2026). The proposed system was compared to the existing system based on energy availability, power continuity, grid dependency, system reliability, monitoring accuracy, forecasting accuracy and control performance. The results demonstrated that the proposed system improved energy availability (by 51.6%; 62.4 ± 5.1% to 94.6 ± 2.3%), power continuity (by 56.1%; 14.8 ± 3.2 to 23.1 ± 1.1 hours/day), reduced grid dependency (by 61.5%; 100% to 38.5 ± 4.6%) and increased system reliability (by 38.6%; 68.7 ± 6.0% to 95.2 ± 2.0%). IoT-based monitoring reported 97.8% accuracy and 84.7% reduction in transmission delay while the machine-learning forecasting reported 93.4% accuracy and 65.5% reduction in mean absolute error. The intelligent control improved load-balancing accuracy from 71.8% to 96.5%, representing a 34.4% improvement, and reduced energy wastage by 72%. A significant difference was observed between the systems (p < 0.001) with large effect sizes. The study concluded that predictive-intelligent SEMS could enhance energy availability and utilization in institutions using grid and solar. &nbsp;

Keywords: Smart Energy Management System, Solar Photovoltaic, Predictive Control, Machine Learning, IoT Monitoring, Load Prioritization.

Obonyano Kingdom Nelson, Onyemelife Ezeh Solomon, Joseph Mene, & Ihieabiaobini Sylvester (2026). Design and Implementation of a Smart Energy Management System for Solar-Powered Buildings Using Predictive Energy Control: A Case Study of University of Delta, Agbor, Delta State. Global Journal of Engineering and Technology Research, 2(9), 483-494. https://doi.org/10.65150/EP-gjetr/V2E9/2026-08

@article{Obonyano2026,
  title   = {Design and Implementation of a Smart Energy Management System for Solar-Powered Buildings Using Predictive Energy Control: A Case Study of University of Delta, Agbor, Delta State},
  author  = {Obonyano Kingdom Nelson and Onyemelife Ezeh Solomon and Joseph Mene and Ihieabiaobini Sylvester},
  journal = {Global Journal of Engineering and Technology Research},
  year    = {2026},
  volume  = {2},
  number  = {9},
  pages   = {483-494},
  doi     = {10.65150/EP-gjetr/V2E9/2026-08},
  url     = {https://doi.org/10.65150/EP-gjetr/V2E9/2026-08}
}

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