TY - GEN
T1 - Feature extraction and background information detection method using power demand
AU - Yoshida, Masahiro
AU - Imanishi, Tomoya
AU - Nishi, Hiroaki
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially supported by MEXT/JSPS KAKENHI Grant (B) Number JP16H04455, through funding received from SECOM Science and Technology Foundation, from an MLIT Grant for development of advanced technology in housing and buildings, by the R&D project “Design of Information and Communication Platform for Future Smart Community Services” by the Ministry of Internal Affairs and Communications of Japan, and from Keio Univ. Global Smart Society Creation Project Research.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/3
Y1 - 2017/8/3
N2 - Recently, many electricity retailers have been aggregating consumer power demand information from smart meter infrastructure, and applications that utilize these data are widely studied. For example, the estimation of customers' background information using their power demand information has attracted much interest; this information can be utilized in the marketing field for background-targeted advertising. In order to utilize power demand information effectively, an appropriate feature extraction method must be applied. In this paper, appropriate data extraction methods specific to power demand information are proposed. In the experiment, power demand data for Kawasaki city were used, and 19 feature data were extracted using the proposed method. The utility of the extracted features was assessed through the performance of classification estimation for two background information types, family structure and floor space. The classification problems are solved by applying two typical machine-learning algorithms, the support vector machine and k-nearest neighbor. In particular, analysis of variance (ANOVA) was applied to the 19 feature data, which were ranked according to the F value. Then, the n (n = [1, 2, 19]) best feature data were used as the input step by step, and the score for each condition was computed to derive the best feature set. According to the results, some of the feature data were considered to be irrelevant, and the best feature data set was successfully selected. Furthermore, thee scores when raw data were input were also computed and compared with the scores when the best feature data set was used. As a result, the performance was better when using processed data instead of raw data.
AB - Recently, many electricity retailers have been aggregating consumer power demand information from smart meter infrastructure, and applications that utilize these data are widely studied. For example, the estimation of customers' background information using their power demand information has attracted much interest; this information can be utilized in the marketing field for background-targeted advertising. In order to utilize power demand information effectively, an appropriate feature extraction method must be applied. In this paper, appropriate data extraction methods specific to power demand information are proposed. In the experiment, power demand data for Kawasaki city were used, and 19 feature data were extracted using the proposed method. The utility of the extracted features was assessed through the performance of classification estimation for two background information types, family structure and floor space. The classification problems are solved by applying two typical machine-learning algorithms, the support vector machine and k-nearest neighbor. In particular, analysis of variance (ANOVA) was applied to the 19 feature data, which were ranked according to the F value. Then, the n (n = [1, 2, 19]) best feature data were used as the input step by step, and the score for each condition was computed to derive the best feature set. According to the results, some of the feature data were considered to be irrelevant, and the best feature data set was successfully selected. Furthermore, thee scores when raw data were input were also computed and compared with the scores when the best feature data set was used. As a result, the performance was better when using processed data instead of raw data.
KW - ANOVA
KW - Feature extraction
KW - K nearest neighbor
KW - Power demand
KW - Smart meter
KW - Support vector machine
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U2 - 10.1109/ISIE.2017.8001439
DO - 10.1109/ISIE.2017.8001439
M3 - Conference contribution
AN - SCOPUS:85029896669
T3 - IEEE International Symposium on Industrial Electronics
SP - 1336
EP - 1341
BT - Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Symposium on Industrial Electronics, ISIE 2017
Y2 - 18 June 2017 through 21 June 2017
ER -