Improved Model for Solar Heating of Buildings

Bernt Lie
Telemark University College, Porsgrunn, Norway

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp15119299

Ingår i: Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden

Linköping Electronic Conference Proceedings 119:30, s. 299-308

Visa mer +

Publicerad: 2015-11-25

ISBN: 978-91-7685-900-1

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


At Telemark University College, Faculty of Technology, on-going work deals with energy efficiency of heating residential buildings. The work ranges from modeling to control of such buildings. Of particular interest in this study is the possibility to “harvest” solar energy via a solar collector, and use this energy to heat the building. Such heating must be combined with auxiliary heating, e.g. electric heating. Lie et al. (2014) discuss basic models for the complete system, with model parameters and operating conditions. For control, the following question is of interest: if it is possible to predict solar irradiation, can this be used to improve the efficiency of energy use in the building? In other words: if we know that tomorrow will by sunny, can we then reduce electric heating during the night based on the knowledge that tomorrow’s sun will re-heat the stored water? Or vice versa: if we know that tomorrow will be cloudy, can we use this fact to heat the stored water in the night when the electricity is cheap? This problem is studied in Beyer et al. (2014). In the discussion of Lie et al. (2014), the model is too simplistic and needs some refinement. The following improvements are needed, in order of importance: 1. Heat integration during ventilation should be included. Currently, the major heat loss is via ventilation. 2. The water storage model should be improved to include the effect of stratification in the tank (the tank is not well mixed) – either by some plug flow model or by dividing the storage tank into 2-3 compartments. 3. The Under Floor Heating (UFH) model is too simple. 4. Heat transfer should be computed from correlation models instead of using fixed numbers. 5. The solar collector model can be improved. In this first improvement of the model, the two first points will be addressed. Thus, the model will be modified to include heat integration. Next, an improved water storage model will be developed to better capture the observed fact that hot water in water storage tanks have a temperature typically at 90C. The resulting improved model will be compared to the model of Lie et al. (2014). References Lie, B., Pfeiffer, C., Skeie, N.-O., Beyer, H.-G. (2014). “Models for Solar Heating of Buildings”. Proceedings, 55th International Conference of Scandinavian Simulation Society (SIMS 2014), October 21-22 2014, Aalborg University, Denmark. Beyer, H.-G., Lie, B., Pfeiffer, C., Arachchige, D.D. (2014). “Using history based probabilistic irradiance forecasts for supporting the predictive control of solar thermal systems”. Proceedings, EuroSun 2014, September 16– 9, Aix-les-Bains, France.


Building model; Dynamics; Simulation; Water storage model; Ventilation heat integration


[1] Arachchige, D.D.K. (2014). An Approach to Day Ahead Forecasting of Solar Irradiance with an Application to Energy Gain in Solar Thermal Collectors. M.Sc. thesis, University of Agder, Faculty of Engineering and Science, Grimstad.

[2] Bayón, R., and Rojas, E. (2013). “Simulation of thermocline storage for solar thermal power plants: From dimensionless results to prototypes and real-size tanks”. International Journal of Heat and Mass Transfer, Vol. 60, pp. 713–721. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2013.01.047.

[3] Defra (2008). Measurement of Domestic Hot Water Consumption in Dwellings. Department for Environment, Food and Rural Affairs (Defra), UK.

[4] de Oliveira , V., Jäschke, J., and Skogestad, S. (2013). “Dynamic online optimization of a house heating system in a fluctuating energy price scenario”. Preprints of the 10th IFAC International Symposium on Dynamics and Control of Process Systems, The International Federation of Automatic Control, December 18-20, 2013, Mumbai, India, pp. 463—468.

[5] Duffie, J.A., and Beckman, W.A. (2013). Solar Engineering of Thermal Processes, 4th edition. JohnWiley& Sons, Hoboken, NJ.

[6] Durão, B., Joyce, A., Farinha Mendes, J. (2014). “Optimization of a seasonal storage solar system using Genetic Algorithms”. Solar Energy, Vol. 101, pp. 160– 166.

[7] Eicker, U. (2014). Energy Efficient Buildings with Solar and Geothermal Resources. John Wiley & Sons Ltd., Chichester, UK. ISBN 9781118352243.

[8] Han, Y.M., Wang, R.Z., and Dai, Y.J. (2009). “Thermal stratification within the water tank”. Renewable and Sustainable Energy Reviews, Vol. 13, pp. 1014– 1026. doi: 10.1016/j.rser.2008.03.001.

[9] Cheng Hin, J.N., and Zmeureanu, R. (2014). “Optimization of a residential solar combisystem for minimum life cycle cost, energy use and exergy destroyed”. Solar Energy, Vol. 100, pp. 102–113.

[10] Holth, E. (2009). Model Predictive Control of mixed solar and electric heating. MSc thesis, NTNU, Norway.

[11] Jordan, U., and Vajen, K. (2000). “Influence of the DHW load profile on the fractional energy savings: a case study of a solar combi-system with trnsys simulations”. Solar Energy, Vol. 69, Nos. 1–6, pp. 197-208.

[12] Kratzenberg, M.G., Beyer, H.G., and Colle, S. (2006). “Uncertainty calculation applied to different regression methods in the quasi-dynamic collector test”. Solar Energy, Vol. 80, pp. 1453–1462

[13] Lie, B., Pfeiffer, C., Skeie, N.-O., Beyer, H.-G. (2014). “Models for Solar Heating of Buildings”. Proceedings, 55th International Conference of Scandinavian Simulation Society (SIMS 2014), October 21-22 2014, Aalborg University, Denmark. Published by Linköping Electronic Press, www.ep.liu.se/ecp/108/ecp14108.pdf, pp. 28–38

[14] Lie, B., Pfeiffer, C., Beyer, H.-G. (2014). “Using history based irradiance forecasts for supporting the predictive control of solar thermal systems”. Proceedings, EuroSun 2014, September 16– 19, Aix-les-Bains, France.

[15] Perera., D.W.U., Pfeiffer, C., and Skeie, N.-O. (2014). “Modelling the heat dynamics of a residential building unit: Application to Norwegian buildings”. Modeling, Identication and Control, Vol. 35, No. 1, pp. 43–57, ISSN 1890-1328. doi: 10.4173/mic.2014.1.4.

[16] Perera., D.W.U., Pfeiffer, C., and Skeie, N.-O. (2014). “Modeling and simulation of multi zone buildings for better control”. Proceedings, SIMS 2014, Aalborg, Denmark, October 21–22, 2014.

[17] Pichler, M.F., Lerch, W., Heinz, A., Goertler, G., Schranzhofer, H., Rieberer, R. (2014). “A novel linear predictive control approach for auxiliary energy supply to a solar thermal combistorage”. Solar Energy, Vol. 101, pp. 203–219.

[18] Powell , K.M., and Edgar, T.F. (2012). “Modeling and control of a solar thermal power plant with thermal energy storage”. Chemical Engineering Science, Vol.
71, pp. 138–145.

[19] Powell , K.M., and Edgar, T.F. (2013). “An adaptivegrid model for dynamic simulation of thermocline thermal energy storage systems”. Energy Conversion and Management, Vol. 76, pp. 865–873. http://dx.doi.org/10.1016/j.enconman.2013.08.043.

[20] Saleh, A.M. (2012). Modeling of Flat-Plate Solar Collector Operation in Transient States. M.Sc. thesis, Purdue University, Indiana.

[21] Zima,W., and Dziewa, P. (2010). “Mathematical modelling of heat transfer in liquid flat-plate solar collector tubes”. Archives of Thermodynamics, Vol. 31, No. 2, pp. 45—62. doi: 10.2478/v10173-010-0008-7.

Citeringar i Crossref