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Energy load predictions for buildings based on a total demand perspective
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
1998 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 28, no 1, 109-116 p.Article in journal (Refereed) Published
Abstract [en]

The outline of this work was to develop models for single family buildings, based on a total energy demand perspective, i.e., building-climate-inhabitants. The building-climate part was included by using a commercial dynamic energy simulation software. Whereas the influence from the inhabitants was implemented in terms of a predicted load for domestic equipment and hot water preparation, based on a reference building. The estimations were processed with neural network techniques. All models were based on access to measured diurnal data from a limited time period, ranging from 10 to 35 days. The annual energy predictions were found to be improved, compared to models based on only a building-climate perspective, when the domestic load was included. For periods with a small heating demand, i.e., May-September, the average accuracy was 7% and 4% for the heating and total energy load, respectively, whereas for the rest of the year the accuracy was on average 3% for both heating and total energy load.

Place, publisher, year, edition, pages
Elsevier, 1998. Vol. 28, no 1, 109-116 p.
Keyword [en]
neutral network, building energy prediction, inhabitant behaviour
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Physical Sciences Building Technologies Energy Engineering
Identifiers
URN: urn:nbn:se:umu:diva-38898DOI: 10.1016/S0378-7788(98)00009-7OAI: oai:DiVA.org:umu-38898DiVA: diva2:384328
Available from: 2011-01-08 Created: 2011-01-08 Last updated: 2015-11-13Bibliographically approved
In thesis
1. Building energy load predictions: Based on neural network techniques
Open this publication in new window or tab >>Building energy load predictions: Based on neural network techniques
1997 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
umeå: Umeå universitet, 1997. 84 p.
National Category
Engineering and Technology
Identifiers
urn:nbn:se:umu:diva-51928 (URN)91-7191-358-0 (ISBN)
Presentation
1997-09-15, Teknikhuset, Umeå universitet, Umeå, 15:00 (Swedish)
Supervisors
Available from: 2012-02-06 Created: 2012-02-04 Last updated: 2012-02-06Bibliographically approved

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Olofsson, ThomasAndersson, StaffanÖstin, Ronny

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Energy and Buildings
Electrical Engineering, Electronic Engineering, Information EngineeringPhysical SciencesBuilding TechnologiesEnergy Engineering

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