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Dynamic Prediction of the Heat Demand for Buildings in District Heating Systems
KTH, School of Electrical Engineering (EES), Communication Theory.
KTH, School of Industrial Engineering and Management (ITM), Industrial Ecology (moved 20130630).
Mälardalen University, Sweden. (School of Business, Society and Energy)
Tongji University, China. (School of Electronics and Information Engineering)
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2013 (English)In: Proceedings of the 5th International Conference on Applied Energy, 2013Conference paper, Published paper (Refereed)
Abstract [en]

Heat demand is a key parameter for optimizing district heating (DH) systems. A mathematical model employing the Gaussian mixture model (GMM) was developed in order to predict the heat demand in DH systems on the consumer side. Prediction of heat demands needs to consider outdoor temperature and people’s social behaviors. In order to precisely consider the effects of social behaviors, the buildings in DH systems were classified into three types: commercial buildings, office buildings and apartment buildings. The model was trained and validated based on the water flow rate and the temperatures of supply and return water in the DH system. The results showed that the model can predict the heat demand in DH systems with an uncertainty between 4-9%. According to the heat demand predicted by the developed model, the potentials of energy saving in the DH system were analyzed.

Place, publisher, year, edition, pages
2013.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Environmental Engineering Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-122143OAI: oai:DiVA.org:kth-122143DiVA: diva2:620979
Conference
5th International Conference on Applied Energy (ICAE); Pretoria, South Africa, 1–4 July 2013
Note

QC 20130828

Available from: 2013-05-13 Created: 2013-05-13 Last updated: 2013-08-28Bibliographically approved

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CiteExportLink to record
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