In this study, we analyze multidimensional data collected from a large number of sensors installed in an active-learning space in a university through topological data analysis (TDA). Further, we propose a method to examine this data visually. The proposed method visualizes the relationship between electricity usages and observes carbon dioxide (CO₂) concentration data, extracting the features of electricity usage, as well as the factors contributing to the increase in CO₂. This method, which relies on TDA, clarifies the data structure and expresses the correspondence with the original data, which is effective for the visualization and application of multidimensional time-series data. Correlations were found between CO₂levels, energy consumption, and the presence of people.