Ishizu et al., 2020 - Google Patents
Home activity recognition using aggregated electricity consumption dataIshizu et al., 2020
- Document ID
- 4021591972105794590
- Author
- Ishizu K
- Mizumoto T
- Yamaguchi H
- Higashino T
- Publication year
- Publication venue
- 2020 IEEE International Conference on Smart Computing (SMARTCOMP)
External Links
Snippet
In this paper, we propose a low-cost, non-invasive home activity recognition method using low-resolution power consumption data. Notably, we tackle the following two challenges. Firstly, we use only the time series of power consumption data aggregated per house and …
- 230000000694 effects 0 title abstract description 83
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9104189B2 (en) | Methods and apparatuses for monitoring energy consumption and related operations | |
| Lin et al. | Applying power meters for appliance recognition on the electric panel | |
| Kleiminger et al. | Occupancy detection from electricity consumption data | |
| Tsai et al. | Modern development of an adaptive non-intrusive appliance load monitoring system in electricity energy conservation | |
| Ma et al. | Toward energy-awareness smart building: Discover the fingerprint of your electrical appliances | |
| TWI526852B (en) | Method for calculating number of people based on state of use of electrical appliances and monitoring system thereof | |
| Aquino et al. | Characterization of electric load with information theory quantifiers | |
| Clement et al. | Detecting activities of daily living with smart meters | |
| de Diego-Otón et al. | Recurrent LSTM architecture for appliance identification in non-intrusive load monitoring | |
| Nakagawa et al. | Toward real-time in-home activity recognition using indoor positioning sensor and power meters | |
| Lee et al. | Personalized energy auditor: Estimating personal electricity usage | |
| Hung et al. | Abnormality detection for improving elder’s daily life independent | |
| Alhamoud et al. | Extracting human behavior patterns from appliance-level power consumption data | |
| Gao et al. | Occupancy detection in smart housing using both aggregated and appliance-specific power consumption data | |
| Lin | An advanced smart home energy management system considering identification of ADLs based on non-intrusive load monitoring | |
| Zhou et al. | Recognizing occupant presence status in residential buildings from environment sensing data by data mining approach | |
| Yin et al. | Unsupervised daily routine and activity discovery in smart homes | |
| Chen et al. | Energy prediction based on resident's activity | |
| Al-Khadher et al. | An implementation framework overview of non-intrusive load monitoring | |
| Ishizu et al. | Home activity recognition using aggregated electricity consumption data | |
| Makonin | Approaches to non-intrusive load monitoring (nilm) in the home | |
| Ishizu et al. | Home Activity Pattern Estimation Using Aggregated Electricity Consumption Data. | |
| Suryadevara et al. | Wellness determination of inhabitant based on daily activity behaviour in real-time monitoring using sensor networks | |
| Batra et al. | How good is good enough? re-evaluating the bar for energy disaggregation | |
| Vafeiadis et al. | Energy-based decision engine for household human activity recognition |