Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Disaggregation Model for Studying Behaviours in Power Consumption
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A feature of the Smart Grid is the utilization of flexible load in the power system. The presence of flexible load allows part of the power consumption to be shifted from peak hours to off-peak hours; this change in power consumption is called a load shift. If the usage pattern of appliances is identified, it is possible to estimate the capacity of a potential load shift as well as evaluate if the utilization of flexible load in the power system results in a load shift. This master thesis project aims to create a model which works as an aid when studying usage patterns by identifying when appliances that contribute to the load shift are active. The model should be able to give approximations of the switch-on and switch-off time of the appliances using only information from a single meter that measures the total power consumption of the entire household.

Recently, artificial neural networks have been successfully applied to these kinds of problems. The constructed model thus includes neural networks which regress the start time and end time of a target appliance. The networks are trained and evaluated both on simulated data and on real measured data from the Stockholm Royal Seaport project. The model is able to give highly accurate estimates of the start and stop time when trained with simulated data. When using real data the accuracy of the model is relatively low. In order to increase the performance the neural network part of the model has to be trained on a larger dataset.

A study of how the sampling time of the input affects the performance of the model is also carried out. The results show no evidence that the sampling time affects the accuracy of the model. However, the architecture of the neural networks trained to recognize data with different sampling frequencies are not identical; if the pooling layers of all networks were removed it might be possible to establish a connection between sampling time and performance.

Place, publisher, year, edition, pages
2017. , p. 46
Series
UPTEC ES, ISSN 1650-8300 ; 17 040
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:uu:diva-331619OAI: oai:DiVA.org:uu-331619DiVA, id: diva2:1149514
External cooperation
KTH; Ellevio
Presentation
2017-09-27, 12:17 (Swedish)
Supervisors
Examiners
Available from: 2017-10-20 Created: 2017-10-16 Last updated: 2017-10-20Bibliographically approved

Open Access in DiVA

fulltext(1798 kB)36 downloads
File information
File name FULLTEXT01.pdfFile size 1798 kBChecksum SHA-512
17472d13ce212584d7cc0ee5cb0241a4f27e20e123c7d9d74bc40774d37dc89317c43452ed557639f4371f0aa3a087284f2f5d1af6f346128ecf94a45c1c380a
Type fulltextMimetype application/pdf

Energy Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 36 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 156 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf