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Predictions of the heat consumption in a low-energy building usingan artificial neural network
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Energy Processes.
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Energy Processes.ORCID iD: 0000-0003-3315-4201
(English)Manuscript (preprint) (Other academic)
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-143478OAI: oai:DiVA.org:kth-143478DiVA: diva2:706603
Note

QS 2014

Available from: 2014-03-21 Created: 2014-03-21 Last updated: 2014-03-21Bibliographically approved
In thesis
1. Low-energy buildings: energy use, indoor climate and market diffusion
Open this publication in new window or tab >>Low-energy buildings: energy use, indoor climate and market diffusion
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Low-energy buildings have, in recent years, gained attention and moved towards a large-scale introduction in the residential sector. During this process, national and international criteria for energy use in buildings have become stricter and the European Union has through the Energy Performance of Buildings Directive imposed on member states to adapt their building regulations for ‘Nearly Zero Energy Buildings’, which by 2021 should be standard for new buildings.

With a primary focus on new terraced and detached houses, this thesis analyses how the concept of low-energy buildings may be further developed to reduce the energy use in the residential sector. The main attention is on the technical performance in terms of indoor climate and heat consumption as well as on the market diffusion of low-energy buildings into the housing market.

A multidisciplinary approach is applied, which here means that the concept of low-energy buildings is investigated from different perspectives as well as on different system levels. The thesis thus encompasses methods from both engineering and social sciences and approaches the studied areas through literature surveys, interviews, assessments and simulations.

The thesis reveals how an increased process integration of the building’s energy system can improve the thermal comfort in low-energy buildings. Moreover, it makes use of learning algorithms – in this case artificial neural networks – to study how the heat consumption can be predicted in a low-energy building in the Swedish climate. The thesis further focuses on the low-energy building as an element in our society and it provides a market diffusion analysis to gain understanding of the contextualisation process. In addition, it suggests possible approaches to increase the market share of low-energy buildings.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. 64 p.
Series
TRITA-CHE-Report, ISSN 1654-1081 ; 2014:5
Keyword
building energy simulations, energy efficiency gap, energy use, indoor climate, low-energy buildings
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-143480 (URN)978-91-7595-019-8 (ISBN)
Public defence
2014-03-28, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20140321

Available from: 2014-03-21 Created: 2014-03-21 Last updated: 2016-03-01Bibliographically approved

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Grönkvist, Stefan

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