The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Smart Meter Driven Segmentation: What Your Consumption Says About You
Abstract
With the rollout of smart metering infrastructure at scale, demand-response (DR) programs may now be tailored based on users' consumption patterns as mined from sensed data. For issuing DR events it is key to understand the inter-temporal consumption dynamics as to appropriately segment the user population. We propose to infer occupancy states from consumption time series data using a hidden Markov model framework. Occupancy is characterized in this model by 1) magnitude, 2) duration, and 3) variability. We show that users may be grouped according to their consumption patterns into groups that exhibit qualitatively different dynamics that may be exploited for program enrollment purposes. We investigate empirically the information that residential energy consumers' temporal energy demand patterns characterized by these three dimensions may convey about their demographic, household, and appliance stock characteristics. Our analysis shows that temporal patterns in the user's consumption data can predict with good accuracy certain user characteristics. We use this framework to argue that there is a large degree of individual predictability in user consumption at a population level.
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Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
Wang, Y., Chen, Q., Hong, T., & Kang, C. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2818167 · Cited by 1,369
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
Glucotypes reveal new patterns of glucose dysregulation
Hall, H., Perelman, D., Breschi, A., Limcaoco, P., Kellogg, R., McLaughlin, T., & Snyder, M. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2005143 · Cited by 326
From demand response to integrated demand response: review and prospect of research and application
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Revealing household characteristics from smart meter data
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Metadata
- Authors
- Albert, A., & Rajagopal, R.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2013-11
- Volume
- 28
- Issue
- 4
- Pages
- 4019-4030
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2028348615
- Cited by
- 269
- References
- 20
- FWCI
- 9.39
- Type
- journal article
- Language
- en
- Open access
- closed
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