Digital Technologies and Smart Energy Management for Nigerian Commercial Buildings: Theoretical and Empirical Contributions

Smart energy management systems integrating digital sensors, artificial intelligence-driven control algorithms, and Internet of Things connectivity represent a transformative technological paradigm for commercial building energy management, yet the theoretical foundations for their adoption in Nigerian commercial buildings and empirical evidence on their performance under local conditions of electricity unreliability, equipment heterogeneity, and institutional constraints are absent from the literature. This study makes original theoretical and empirical contributions to knowledge on smart energy management systems in Nigerian commercial buildings, developing a Contextually-Adaptive Smart Energy Management Theory that extends conventional smart building theory to account for hybrid grid-generator-solar energy supply configurations, frequent power quality disturbances, and variable occupancy patterns characteristic of Nigerian commercial environments. The empirical programme involves instrumented monitoring of 12 commercial buildings in Lagos, Abuja, and Enugu equipped with custom smart energy management system prototypes developed for the study, collecting real-time data on electricity consumption, demand patterns, power quality, generator operation, and occupancy for 18 months. Machine learning control algorithms trained on building-specific data are evaluated against rule-based and manual benchmarks for energy reduction and demand peak shaving performance. Results demonstrate that AI-driven smart energy management reduces building energy costs by 26 to 38 percent, primarily through optimised generator dispatch, solar self-consumption maximisation, and demand scheduling. The Contextually-Adaptive framework outperforms standard smart building theory in explaining adoption barriers and performance variance across the studied buildings. Keywords: smart energy management, digital technologies, commercial buildings, Nigeria, artificial intelligence.