Poster abstract: grid-level short-term load forecasting based on disaggregated smart meter data

Open access
Date
2018-02Type
- Other Journal Item
Abstract
The rollout of smart meters and steadily increasing sample rates lead to a growing amount of raw data available for short-term load forecasting (STLF). While the original motivation for high resolutions has been the enabling of non-intrusive load monitoring (NILM), so far their value for STLF has been limited. We propose a novel approach, which allows the exploitation of high resolution data for STLF, by incorporating NILM and subsequent clustering of similarly behaving appliances as a preprocessing step. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000224175Publication status
publishedExternal links
Journal / series
Computer Science, Research + DevelopmentVolume
Pages / Article No.
Publisher
SpringerSubject
STLF; NILM; Clustering; Smart gridOrganisational unit
03528 - Mattern, Friedemann (emeritus) / Mattern, Friedemann (emeritus)
Notes
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.More
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