Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Big Data Analytics towards a Retrofitting Plan for the City of Stockholm
KTH, School of Electrical Engineering (EES). KTH, School of Architecture and the Built Environment (ABE). KU Leuven. (Energy Innovation/Industrial Ecology)
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis summarises the outcomes of a Big Data analysis, performed on a set of hourly district heating energy consumption data from 2012 for nearly 15 000 buildings in the City of Stockholm. The aim of the study was to find patterns and inefficiencies in the consumption data using KNIME, a big data analysis tool, and to initiate a retrofitting plan for the city to counteract these inefficiencies. By defining a number of energy saving scenarios, the potential for increased efficiency is estimated and the resulting methodology can be used by other (smart) cities and policy makers to estimate savings potential elsewhere. In addition, the influence of weather circumstances, building location and building types is studied.

In the introduction, a concise overview of the concepts Smart City and Big Data is given, together with their relevance for the energy challenges of the 21st century. Thereafter, a summary of the previous studies at the foundation of this research and a brief theory review of less common methods used in this thesis are presented.

The method of this thesis consisted of first understanding and describing the dataset using descriptive statistics, studying the annual fluctuations in energy consumption and clustering all consumer groups per building class according to total consumption, consumption intensity and time of consumption. After these descriptive steps, a more analytical part starts with the definition of a number of energy saving scenarios. They are used to estimate the maximal potential for energy savings, regardless of actual measures, financial or temporal aspects.

This hypothetical simulation is supplemented with a more realistic retrofitting plan that explores the feasibility of Stockholm’s Climate Action Plan for 2012-2015, using a limited set of energy efficiency measures and a fixed investment horizon. The analytical part is concluded with a spatial regression that sets out to determine the influence of wind velocity and temperature in different parts of Stockholm.

The conclusions of this thesis are that the potential for energy savings in the studied data set can go up to 59% or 4.6 TWh. The financially justified savings are estimated at ca. 6% using favourable investment parameters. However, these savings quickly diminish because of a high sensitivity on the input parameters. The clustering analysis has not yielded the anticipated results, but they can be used as a tool to target investments towards groups of buildings that have a high return on investment. 

Place, publisher, year, edition, pages
2014. , 135 p.
Series
TRITA-IM-EX, 2014:08
Keyword [en]
Big Data, District heating, Energy efficiency, Energy savings, Smart cities, GIS, Stockholm
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:kth:diva-155742OAI: oai:DiVA.org:kth-155742DiVA: diva2:762525
Educational program
Master of Science in Engineering - Energy and Environment
Presentation
2014-06-03, Teknikringen 34, Stockholm, 09:00 (English)
Supervisors
Examiners
Available from: 2014-11-13 Created: 2014-11-12 Last updated: 2014-11-13Bibliographically approved

Open Access in DiVA

Thesis_BramvanderHeijde(49434 kB)802 downloads
File information
File name FULLTEXT01.pdfFile size 49434 kBChecksum SHA-512
a64e40de597fe18fe1027881fefae0e2119d049da8eb3f16d0c484649d5eb7284d8a28424c8d3e5c9001f996d10eb385337c2068ab612eafa87153163ce89845
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering (EES)School of Architecture and the Built Environment (ABE)
Energy Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 802 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: 569 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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