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Long term energy demand predictions for buildings based on short-term measured data
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.
2001 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 33, no 2, 85-91 p.Article in journal (Refereed) Published
Abstract [en]

In order to obtain long-term predictions based on short-term data, a neural network model was developed. The model parameters are indoor and outdoor temperature difference and energy for heating and internal use. For purposes of training the neural network model a method for extending the measured data to represent an annual variation is proposed. The method has been applied on six single-family buildings.

Based on access to data from 2 to 5 weeks, the deviation between predicted and measured diurnal energy demand on an annual basis was about 4% with a correlation of 90–95%, when access to the indoor and outdoor temperature difference was assumed. For models based on access to data from the warmest periods with a very small heating demand, the deviation was about 2–4 times larger.

Place, publisher, year, edition, pages
2001. Vol. 33, no 2, 85-91 p.
Keyword [en]
neural network, building energy prediction, occupied single-family buildings, measured data, northern Sweden
Identifiers
URN: urn:nbn:se:umu:diva-38896DOI: 10.1016/S0378-7788(00)00068-2OAI: oai:DiVA.org:umu-38896DiVA: diva2:384326
Available from: 2011-01-08 Created: 2011-01-08 Last updated: 2017-12-11Bibliographically approved

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Olofsson, ThomasAndersson, Staffan
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