Long term energy demand predictions for buildings based on short-term measured data
2001 (English)In: Energy and Buildings, ISSN 0378-7788, Vol. 33, no 2, 85-91 p.Article in journal (Refereed) Published
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.
neural network, building energy prediction, occupied single-family buildings, measured data, northern Sweden
IdentifiersURN: urn:nbn:se:umu:diva-38896DOI: 10.1016/S0378-7788(00)00068-2OAI: oai:DiVA.org:umu-38896DiVA: diva2:384326