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Building energy load predictions: Based on neural network techniques
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
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: urn:nbn:se:umu:diva-51928ISBN: 91-7191-358-0 (print)OAI: oai:DiVA.org:umu-51928DiVA: diva2:490222
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
List of papers
1. Predictions of energy demand in buildings using neural network techniques on performance data
Open this publication in new window or tab >>Predictions of energy demand in buildings using neural network techniques on performance data
1996 (English)In: Proceedings of the 4th fourth symposium on building physics in the nordic countries, 1996, 51-58 p.Conference paper, Published paper (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:umu:diva-38908 (URN)
Conference
4th fourth symposium on building physics in the nordic countries, 1996
Available from: 2011-01-08 Created: 2011-01-08 Last updated: 2012-02-06Bibliographically approved
2. A method for predicting the annual building heating demand based on limmited performance data
Open this publication in new window or tab >>A method for predicting the annual building heating demand based on limmited performance data
1998 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 28, no 1, 101-108 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we present an investigation of the possibility to use a neural network combined with a quasi-physical description in order to predict the annual supplied space heating demand (P) for a number of small single family buildings located in the North of Sweden. As a quasi-physical description for P, we used measured diurnal performance data from a similar building or simulated data from a steady state energy simulation software. We show that the required supplied space heating demand may be predicted with an average accuracy of 5%. The predictions were based on access to measured diurnal data of indoor and outdoor temperatures and the supplied heating demand from a limited time period, ranging from 10 to 35 days. The prediction accuracy was found to be almost independent of what time of the year the measurements were obtained from, except for periods when the supplied heating demand was very small. For models based on measurements from May and fo some buildings from April and September, the prediction was less accurate.

Place, publisher, year, edition, pages
Elsevier, 1998
Keyword
neutral network, building energy prediction, principal component analysis
National Category
Energy Engineering Physical Sciences Building Technologies Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:umu:diva-38899 (URN)10.1016/S0378-7788(98)00004-8 (DOI)
Available from: 2011-01-08 Created: 2011-01-08 Last updated: 2017-12-11Bibliographically approved
3. Energy load predictions for buildings based on a total demand perspective
Open this publication in new window or tab >>Energy load predictions for buildings based on a total demand perspective
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
Keyword
neutral network, building energy prediction, inhabitant behaviour
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Physical Sciences Building Technologies Energy Engineering
Identifiers
urn:nbn:se:umu:diva-38898 (URN)10.1016/S0378-7788(98)00009-7 (DOI)
Available from: 2011-01-08 Created: 2011-01-08 Last updated: 2017-12-11Bibliographically approved

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Olofsson, Thomas

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