DOI: 10.12688/f1000research.182125.2 ISSN: 2046-1402

Intelligent Energy Consumption Model for a Smart Building

Chidi Betrand, Mercy Benson-Emenike, Oluchukwu Ekwealor, Chinwe Onukwugha, Douglas Kelechi, Donatus Njoku, Christopher Ofoegbu, Toochi Ewunonu, Nneka Oragba
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Background The increasing deployment of smart meters in buildings has created opportunities for data-driven energy management, but the detection of abnormal electricity consumption remains challenging because energy-use patterns vary with time, operational schedules, and environmental conditions. These smart meters and connected monitoring technologies continuously create electricity consumption data, which can be used for energy management, fault detection, operational monitoring, and predictive maintenance. Methods In order to monitor building electricity consumption, this study developed an intelligent anomaly detection system based on a Support Vector Machine (SVM) with a radial basis function (RBF) kernel. Electricity consumption data sourced from smart meters was used as the developed model achieved reliable anomaly detection, identifying irregular energy consumption with improved detection and achieved dependable anomaly detection. Results The overall performance metrics of the model was Accuracy of 97.79%, with a macro-averaged F1 Score of 0.8842, Precision of 0.925, and Recall of 0.8513 The results showed reduced false alarms and enhanced adaptability to changes in energy use patterns. Conclusion The study contributed to energy efficiency, cost reduction, and sustainable building management, while also providing a reliable framework for intelligent energy monitoring in smart building. The benefits extended to facility managers and researchers by offering an adaptable system for smarter energy decision-making.

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