Open AccessDetecting Anomalous Energy Consumption from Profiles
Author(s) -
Hiroto Abe,
Kazuaki Bogaki,
Hom Bahadur Rijal,
Mahito Sugiyama
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/294/1/012072
Subject(s) - consumption (sociology) , energy consumption , energy (signal processing) , outlier , computer science , simple (philosophy) , reduction (mathematics) , environmental economics , data mining , econometrics , artificial intelligence , statistics , economics , engineering , mathematics , social science , philosophy , geometry , epistemology , sociology , electrical engineering
Controlling and reducing electric energy consumption is a critical issue across all over the countries for human wellbeing. However, approaches to achieve energy consumption reduction of individual occupants have not been established yet as both problems of collecting a large amount data of energy consumption and constructing prediction models are challenging. Here we show a case-study of energy consumption analysis, in which households with anomalous energy consumption can be completely detected using a questionnaire about their profiles without seeing actual energy consumption . Our approach is based on simple data mining techniques of outlier detection and decision trees, hence it can be easily implemented in the condominium housing market.