This project explores the relationship between library occupancy (estimated through WiFi connection counts) and electricity consumption (measured from three energy meters in the library). The datasets are processed through data cleaning, timestamp synchronization, missing value imputation, and integration across sources. The analysis is presented using time series plots, scatter plots, and 24-hour daily profiles, with additional comparisons between weekdays and weekends. The goal is to understand library usage patterns, identify peak hours of occupancy, and examine how these patterns align with energy demand.