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Past research has shown that occupancy information can be used to reduce building energy consumption through occupant-based controls and by mitigating wasteful occupant behavior. In this study, we investigate the dynamic relationship between WiFi connection counts (as a proxy to occupancy) and building electricity consumption across four building typologies (office, lab, health center, and library). Our findings based on one year of data show a strong positive linear correlation between electricity consumption and WiFi count across all four building when the building is in operation. The data exploration also indicates higher interactions between occupants with the plug and lighting loads in office and lab space types as compared to in a health center and a library. Next, using principal component analysis (PCA) for feature extraction followed by Density-based spatial clustering of applications with noise (DBSCAN), we show that distinct clusters could be generated, characterized by an increase in the between-cluster variance and smaller within-cluster variation. Lastly, we apply linear regression to manifest how the clustering results can be used to better model the variables.