du.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks
Vise andre og tillknytning
2019 (engelsk)Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 240, s. 276-294Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Occupancy behaviour plays an important role in energy consumption in buildings. Currently, the shallow understanding of occupancy has led to a considerable performance gap between predicted and measured energy use. This paper presents an approach to estimate the occupancy based on blind system identification (BSI), and a prediction model of electricity consumption by an air-conditioning system is developed and reported based on an artificial neural network with the BSI estimation of the number of occupants as an input. This starts from the identification of indoor CO2 dynamics derived from the mass-conservation law and venting levels. The unknown parameters, including the occupancy and model parameters, are estimated by using a frequentist maximum-likelihood algorithm and Bayesian estimation. The second phase is to establish the prediction model of the electricity consumption of the air-conditioning system by using a feed-forward neural network (FFNN) and extreme learning machine (ELM), as well as ensemble models. To analyse some aspects of the benchmark test for identifying the effect of structure parameters and input-selection alternatives, three studies are conducted on (1) the effect of predictor selection based on principal component analysis, (2) the effect of the estimated occupancy as the supplementary input, and (3) the effect of the neural network ensemble. The result shows that the occupancy number, as the input, is able to improve the accuracy in predicting energy consumption using a neural network model.

sted, utgiver, år, opplag, sider
Elsevier, 2019. Vol. 240, s. 276-294
Emneord [en]
Occupancy estimation; Blind system identification (BSI); Prediction model for energy consumption; Feedforward neural network; Extreme learning machine
HSV kategori
Forskningsprogram
Energi och samhällsbyggnad
Identifikatorer
URN: urn:nbn:se:du-29562DOI: 10.1016/j.apenergy.2019.02.056ISI: 000468714300020OAI: oai:DiVA.org:du-29562DiVA, id: diva2:1291293
Tilgjengelig fra: 2019-02-24 Laget: 2019-02-24 Sist oppdatert: 2019-08-26bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Zhang, XingxingHan, Mengjie

Søk i DiVA

Av forfatter/redaktør
Zhang, XingxingHan, Mengjie
Av organisasjonen
I samme tidsskrift
Applied Energy

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 38 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf