Time Series Electricity Consumption Analysis using Non-negative Matrix Factorization

For developing a sustainable society, energy management systems are utilized in many organizations. Chiba University of Commerce (CUC) is one of the organizations that has completely switched to renewable energy-sourced electricity for the first time in Japan. In the campus, energy consumption due to air conditioning, lightning and so on at each room is monitored. These monitoring data are stored on a data server via smart meters. In order to promote awareness to reduce electricity consumption, we need to summarize a vast amount of data so that we can interpret the data easily, and find out where we can afford to save electricity consumption. In this paper, we employ non-negative matrix factorization (NMF) for summarizing time-series electricity consumption patterns to analyze the electricity consumption data over time. Through the data analysis, we show that the visualization of factor matrices by dimensionality reduction enables us easily to interpret the low level electricity consumption data, and it gives us some awareness on energy saving.