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The 'SmartNIALMeter' electrical appliance disaggregation dataset.
Vogel, Manuel; Friedli, Martin; Camenzind, Martin; Kniesel, Guido; Klemenjak, Christoph; Gugolz, Gianni; Huber, Patrick; Calatroni, Alberto; Kaufmann, Lukas; Rumsch, Andreas; Paice, Andrew.
Affiliation
  • Vogel M; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Friedli M; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Camenzind M; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Kniesel G; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Klemenjak C; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Gugolz G; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Huber P; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Calatroni A; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Kaufmann L; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Rumsch A; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
  • Paice A; Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
Data Brief ; 56: 110854, 2024 Oct.
Article in En | MEDLINE | ID: mdl-39286425
ABSTRACT
Electrical disaggregation, also known as non-intrusive load monitoring (NILM) or non-intrusive appliance load monitoring (NIALM), attempts to recognize the energy consumption of single electrical appliances from the aggregated signal. This capability unlocks several applications, such as giving feedback to users regarding their energy consumption patterns or helping distribution system operators (DSOs) to recognize loads which could be shifted to stabilize the electrical grid. The project "SmartNIALMeter" brought together universities, companies and DSOs and involved the collection of a large data corpus comprising 20 buildings with a total of 100 electrical appliances for a period of up to two years at a sampling interval of five seconds. The variability of the loads, including heat pumps and a charging station for electric vehicles, and the presence of single-phase and three-phase devices make this dataset suitable for several investigations. The total consumption was collected through smart meters and each appliance's consumption was measured with a dedicated sensor, providing sub-metering for all loads. The dataset can be used to tackle several open research questions, for example to investigate new NILM algorithms able to learn with a limited amount of sub-metered data.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Data Brief Year: 2024 Document type: Article Affiliation country: Switzerland Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Data Brief Year: 2024 Document type: Article Affiliation country: Switzerland Country of publication: Netherlands