Gregory Provan
Department of Computer Science, University College Cork, Cork, Ireland
Alberto Venturini
Department of Computer Science, University College Cork, Cork, Ireland
Download articlehttp://dx.doi.org/10.3384/ecp12076829Published in: Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany
Linköping Electronic Conference Proceedings 76:85, p. 829-838
Published: 2012-11-19
ISBN: 978-91-7519-826-2
ISSN: 1650-3686 (print), 1650-3740 (online)
The physical modelling and simulation of systems with inherent uncertainty still poses significant issues when using Modelica and its tools. We propose a framework for stochastic simulation using Modelica that incorporates stochastic external inputs to the model. We apply this to a sustainable energy application; optimising the control of underfloor heating in a building.
[1] Daniel Bouskela; Audrey Jardin; Zakia Benjelloun-Touimi; Peter Aronsson; and Peter Fritzson. Modelling of uncertainties with Modelica. In Proceedings of the 8th International Modelica Conference; Dresden; Germany; 2011. Linköping University Electronic Press.
[2] R. Buizza; P. L. Houtekamer; Gerald Pellerin; Zoltan Toth; Yuejian Zhu; and Mozheng Wei. A comparison of the ecmwf; msc; and ncep global ensemble prediction systems. Monthly Weather Review; Vol. 133; No. 5; 2005.
doi: 10.1175/MWR2905.1.
[3] M. Eames; T. Kershaw; and D. Coley. A comparison of future weather created from morphed observed weather and created by a weather generator. Building and Environment; 2012.
doi: 10.1016/j.buildenv.2012.03.006.
[4] H.R. Glahn and D.A. Lowry. The use of model output statistics (mos) in objective weather forecasting. Journal of Applied Meteorology; 11(8):1203–1211; 1972.
doi: 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2.
[5] T. Gneiting; F. Balabdaoui; and A.E. Raftery. Probabilistic forecasts; calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology); 69(2):243–268; 2007.
doi: 10.1111/j.1467-9868.2007.00587.x.
[6] T. Gneiting and A.E. Raftery. Strictly proper scoring rules; prediction; and estimation. Journal of the American Statistical Association; 102(477):359–378; 2007.
doi: 10.1198/016214506000001437.
[7] J.A. Hoeting; D. Madigan; A.E. Raftery; and C.T. Volinsky. Bayesian model averaging: a tutorial. Statistical science; pages 382–401; 1999.
[8] C.J. Hopfe and J.L.M. Hensen. Uncertainty analysis in building performance simulation for design support. Energy and Buildings; 2011.
[9] D. Jacob; S. Burhenne; A.R. Florita; and G.P. Henze. Optimizing building energy simulation models in the face of uncertainty; 2010.
[10] Y. Jiang and T. Hong. Stochastic analysis of building thermal processes. Building and Environment; 28(4):509–518; 1993.
doi: 10.1016/0360-1323(93)90027-Z.
[11] B. Johansson and P. Krus. Probabilistic analysis and design optimization of modelica models. In Paper presented at the 4th International Modelica Conference; 2005.
[12] I.A. Macdonald. Quantifying the effects of uncertainty in building simulation. PhD thesis; Department of Mechanical Engineering; University of Strathclyde; 2002.
[13] M. Wetter. A modelica-based model library for building energy and control systems. In Proc. of the 11th IBPSA Conference; 2009.