DESIGN AND PERFORMANCE EVALUATION OF AN AI-INTEGRATED SOLAR-POWERED MODULAR BUILDING SYSTEM FOR ENERGY SELF-SUFFICIENT TERTIARY INSTITUTIONS

The persistent energy insecurity confronting Nigerian tertiary institutions — characterised by chronic grid power outages, prohibitive diesel generator costs, and unreliable electricity supply that undermines academic and research productivity — demands a paradigm shift in campus energy infrastructure design. This paper presents the design and performance evaluation of an Artificial Intelligence-Integrated Solar-Powered Modular Building System (AI-SPMBS) specifically conceived for energy self-sufficient tertiary institution campuses in Nigeria and comparable developing-world educational environments. The system integrates photovoltaic solar energy generation, advanced battery energy storage, AI-driven energy management algorithms, and modular prefabricated building construction into a unified, scalable, and digitally intelligent campus infrastructure model. The study is structured around four research objectives: (1) assessment of solar generation capacity and energy self-sufficiency potential; (2) evaluation of AI energy management system optimization effectiveness; (3) performance evaluation of the modular construction approach in terms of speed, cost, and quality; and (4) total cost of ownership and return on investment analysis. The AI-SPMBS prototype, evaluated against Auchi Polytechnic campus as a reference case, achieved a modelled energy self-sufficiency index of 87.3%, a 34% reduction in energy-related operational costs, an AI-optimized load dispatch efficiency of 91.2%, and a modular construction timeline reduction of 42% compared to conventional methods. These results confirm the AI-SPMBS as a transformative infrastructure model for Nigeria's tertiary education sector and a replicable template for the broader developing world.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-30
DOI
https://doi.org/10.5281/zenodo.20281341
Primary Topic
Hybrid Renewable Energy Systems
Type
article
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article

DESIGN AND PERFORMANCE EVALUATION OF AN AI-INTEGRATED SOLAR-POWERED MODULAR BUILDING SYSTEM FOR ENERGY SELF-SUFFICIENT TERTIARY INSTITUTIONS

Daniel ELAMAH, Nkechi Taiwo, Osamudiamen Bamidele
Zenodo (CERN European Organization for Nuclear Research)
Hybrid Renewable Energy Systems
article

DESIGN AND PERFORMANCE EVALUATION OF AN AI-INTEGRATED SOLAR-POWERED MODULAR BUILDING SYSTEM FOR ENERGY SELF-SUFFICIENT TERTIARY INSTITUTIONS

Daniel ELAMAH, Nkechi Taiwo, Osamudiamen Bamidele
article en

Abstract

The persistent energy insecurity confronting Nigerian tertiary institutions — characterised by chronic grid power outages, prohibitive diesel generator costs, and unreliable electricity supply that undermines academic and research productivity — demands a paradigm shift in campus energy infrastructure design. This paper presents the design and performance evaluation of an Artificial Intelligence-Integrated Solar-Powered Modular Building System (AI-SPMBS) specifically conceived for energy self-sufficient tertiary institution campuses in Nigeria and comparable developing-world educational environments. The system integrates photovoltaic solar energy generation, advanced battery energy storage, AI-driven energy management algorithms, and modular prefabricated building construction into a unified, scalable, and digitally intelligent campus infrastructure model. The study is structured around four research objectives: (1) assessment of solar generation capacity and energy self-sufficiency potential; (2) evaluation of AI energy management system optimization effectiveness; (3) performance evaluation of the modular construction approach in terms of speed, cost, and quality; and (4) total cost of ownership and return on investment analysis. The AI-SPMBS prototype, evaluated against Auchi Polytechnic campus as a reference case, achieved a modelled energy self-sufficiency index of 87.3%, a 34% reduction in energy-related operational costs, an AI-optimized load dispatch efficiency of 91.2%, and a modular construction timeline reduction of 42% compared to conventional methods. These results confirm the AI-SPMBS as a transformative infrastructure model for Nigeria's tertiary education sector and a replicable template for the broader developing world.

Zenodo (CERN European Organization for Nuclear Research)
Auchi Polytechnic (NG)
Industry, innovation and infrastructure
Openalex Percentile: Top 14%
Hybrid Renewable Energy Systems
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