Analysis of the Trends Between Indoor Carbon Dioxide Concentration and Plug-Level Electricity Usage Through Topological Data Analysis

Abstract

Sensors, such as environmental sensors and vital sensors collect various timeseries data, however, we hardly analyze all of them because of difficulty in visualization at once. We propose a method to bring out the events of interest from multi-dimensional time-series data, which is difficult to recognize everything, and to facilitate the analysis of factors, and verify its effectiveness. As a case study, we analyze multidimensional timeseries data collected from many sensors equipped in an active-learning space in a university library using topological data analysis (TDA), in particular the Mapper. Furthermore, we propose a method to examine the mapper graph visually and to interpret results for taking advantage of sensing data. The proposed method visualizes the relationship between environment data consists of temperature, humidity, illuminance, human motion, and electricity usages and observes indoor carbon dioxide (CO2) concentration data, extracting the features of environment data as the factors contributing to the increase in CO2. This method, which relies on TDA-Mapper, clarifies the high dimensional 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 CO2 levels, energy consumption, and the motions of people. This method is useful as a simple way to interpret the features of large data sets acquired from many sensors.