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Classification of Power Consumption Patterns for Swedish Households Using K-means
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Industrial Ecology.
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Industrial Ecology.
2016 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Society is facing a big challenge. To achieve a more sustainable development the power distribution system needs to change. The development of Smart Grid is one way of making the electricity market more sustainable. More information about the grid, such as information about where renewable energy sources are installed, is essential for the development of Smart Grids. When new energy sources, for example solar panels, are connected to the grid there will be consequences. Sudden changes in the energy transportation in the grid when the weather changes from sunny to cloudy will affect the balance. The grid owners need to be able to control the grid more actively to compensate the inconsistency of renewable energy sources. One way of handling this is to obtain more information about the end users’ consumption patterns and to analyse this information to create a useful tool for the grid owners. This project aims to propose a method for classification of power consumption profiles for Swedish household by using hourly data from smart meters. The presented method first divides the data according to season and type of day and thereafter it is normalised before it is clustered into typical clusters using the K-means algorithm. To be able to run K-means, the number of clusters needs to be set in advance. The presented method therefore tries to find the optimum number of clusters by controlling the similarities between clusters, using cross correlation. The project shows it is possible to profile Swedish households using K-means. 

Place, publisher, year, edition, pages
2016.
Series
TRITA-IM-KAND 2016:14
National Category
Environmental Management
Identifiers
URN: urn:nbn:se:kth:diva-189060OAI: oai:DiVA.org:kth-189060DiVA: diva2:943160
Available from: 2016-06-27 Created: 2016-06-27 Last updated: 2016-06-27Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Output format
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