A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

People usually spend several hours per day inside buildings, and they require great amounts of energy resources to operate. Although there are numerous studies about smart is still a need for new intelligent techniques efficient building management. This paper proposes the use Wi-Fi network association information as basis design systems buildings. We propose unified experimental methodology evaluate machine learning (ML) models on their capacity accurately predict access point demand energy-efficient The evaluation involves multiple classification regression using variety configurations algorithms. conducted an analysis our proposed determine which ML provide best performance results data collected from large scale located at Fluminense Federal University (UFF) over period 6 months. enables user create management systems. achieved 86.69% accuracy occupancy prediction RMSPE (Root Mean Squared Percentage Error) 0.29 count techniques.