Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Data-Driven Predictions of Heating Energy Savings in Residential Buildings
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för teknikvetenskaper, Tillämpad mekanik, Byggteknik.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för teknikvetenskaper, Tillämpad mekanik, Byggteknik.
2019 (Engelska)Självständigt arbete på avancerad nivå (yrkesexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
Abstract [en]

Along with the increasing use of intermittent electricity sources, such as wind and sun, comes a growing demand for user flexibility. This has paved the way for a new market of services that provide electricity customers with energy saving solutions. These include a variety of techniques ranging from sophisticated control of the customers’ home equipment to information on how to adjust their consumption behavior in order to save energy. This master thesis work contributes further to this field by investigating an additional incentive; predictions of future energy savings related to indoor temperature. Five different machine learning models have been tuned and used to predict monthly heating energy consumption for a given set of homes. The model tuning process and performance evaluation were performed using 10-fold cross validation. The best performing model was then used to predict how much heating energy each individual household could save by decreasing their indoor temperature by 1°C during the heating season. The highest prediction accuracy (of about 78%) is achieved with support vector regression (SVR), closely followed by neural networks (NN). The simpler regression models that have been implemented are, however, not far behind. According to the SVR model, the average household is expected to lower their heating energy consumption by approximately 3% if the indoor temperature is decreased by 1°C. 

Ort, förlag, år, upplaga, sidor
2019. , s. 62
Serie
UPTEC STS, ISSN 1650-8319 ; 19027
Nyckelord [en]
Building Energy, Machine Learning, Energy Savings, Heating Energy, Indoor Temperature, Neural Networks, Support Vector Regression, Random Forest, Ridge Regression, K-Nearest Neighbors
Nationell ämneskategori
Energisystem Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:uu:diva-387395OAI: oai:DiVA.org:uu-387395DiVA, id: diva2:1329457
Externt samarbete
Tibber AS
Utbildningsprogram
Civilingenjörsprogrammet System i teknik och samhälle
Handledare
Examinatorer
Tillgänglig från: 2019-06-24 Skapad: 2019-06-24 Senast uppdaterad: 2019-06-24Bibliografiskt granskad

Open Access i DiVA

fulltext(2194 kB)28 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 2194 kBChecksumma SHA-512
cb0ad285c51717a6629b70d5cf0c9cba2bd211bcfc2aac5037363632a8f942fad34611f1e16fbd40a2586c9bb45d526869d98214283bf72740c311a9a7ad9dc6
Typ fulltextMimetyp application/pdf

Av organisationen
Byggteknik
EnergisystemData- och informationsvetenskap

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 28 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 71 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf