TY - GEN
T1 - Time Series Electricity Consumption Analysis using Non-negative Matrix Factorization
AU - Kusaba, Akira
AU - Kuboyama, Tetsuji
AU - Hashimoto, Takako
N1 - Funding Information:
This work was partially supported by JSPS KAK-ENHI Grant Numbers 18K11443, 19K12125, 19H01133, 19J00871, and 17H00762.
Funding Information:
This work was partially supported by JSPS KAKENHI Grant Numbers 18K11443, 19K12125, 19H01133, 19J00871, and 17H00762.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
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U2 - 10.1109/ICAwST.2019.8923311
DO - 10.1109/ICAwST.2019.8923311
M3 - Conference contribution
AN - SCOPUS:85077772465
T3 - 2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
BT - 2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Awareness Science and Technology, iCAST 2019
Y2 - 23 October 2019 through 25 October 2019
ER -
