Abstract
This paper highlights the problem of forecast model design for time series of heat demand. We propose the forecast model of heat demand based on the assumption that the course of heat demand can be described sufficiently well as a function of the outdoor temperature and the weather independent component (social components). Time of the day affects the social components. Forecast of social component is realized by means of Box-Jenkins methodology. The weather dependent component is modeled as a heating characteristic (function that describes the temperature-dependent part of heat consumption). The principal aim is to derive an explicit expression for the heating characteristics. The Neural Network Synthesis is successfully applied here to find this expression. An experiment described in the paper was realized on real life data. We have studied half-hourly heat demand data, covering four month period in concrete district heating system (DHS) from Most agglomeration and heating plant situated in Komořany, Czech Republic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arvastson, L.: Stochastic modelling and operational optimization in district-heating systems. Doctoral thesis. Lund University, Centre for Mathematical Sciences (2001) ISBN 91-628-4855-0
Bakker, V., Bosman, M.G.C., Molderink, A., Hurink, J.L., Smit, G.J.M.: Improved heat demand prediction of individual households. In: Proceedings of IFAC Conference on Control Methodologies and Technology for Energy Efficiency, CMTEE 2010, Vilamoura, pp. 110–115 (2010) ISBN: 978-390266168-5
Balátě, J.: Design of Automated Control System of Centralized Heat Supply. Thesis of DrSc (Doctor of Science) Work. TU Brno, Faculty of Mechanical Engineering (1982)
Box, G.E., Jenkins, G.M.: Time series analysis: forecasting and control, Rev. edn. Holden-Day, San Francisco (1976) ISBN 0-8162-1104-3.
Chramcov, B.: Utilization of Mathematica environment for designing the forecast model of heat demand. WSEAS Transaction on Heat and Mass Transfer 6(1), 21–30 (2011)
Chramcov, B., Vařacha, P.: Design of a Model for Heat Demand Prediction Using the Neural Network Synthesis. In: Proceedings of the 6th International Conference on Applied Mathematics, Simulation, Modelling (ASM 2012), Athens, Greece (2012)
Chramcov, B.: Heat deman forecasting for concrete district heating system. International Journal of Mathematical Models and Methods in Applied Sciences 4(4), 231–239 (2010)
Dostál, P.: Machine Processing of Daily Diagram Course Prediction of Loading the Centralized Heat Supply System. Doctoral thesis. TU Brno, Faculty of Mechanical Engineering (1986)
Dotzauer, E.: Simple model for prediction of loads in district-heating systems. Applied Energy 73(3-4), 277–284 (2002) ISSN 0306-2619
Koza, J.R., Bennett, F.H., Andre, D., Keane, M.A.: Genetic Programming III; Darwinian Invention and problem Solving. Morgan Kaufmann Publisher (1999) ISBN 1-55860-543-6
Král, E., Dolinay, V., Vašek, L., Vařacha, P.: Usage of PSO Algorithm for Parameters Identification of District Heating Network Simulation Model. In: 14th WSEAS International Conference on Systems, Rhodes, Greece. Latest Trands on Systems, vol. II, pp. 657–659 (2010) ISBN/ISSN: 978-960-474-214-1
Lehtoranta, O., Seppälä, J., Koivisto, H., Koivo, H.: Adaptive district heat load forecasting using neural networks. In: Proceedings of Third International Symposium on Soft Computing for Industry, Maui, USA (2000)
Park, T.C., Kim, U.S., Kim, L.H., Kim, W.H., Yeo, Y.K.: Optimization of district heating systems based on the demand forecast in the capital region. Korean Journal of Chemical Engineering 26(6), 1484–1496 (2009) ISSN 0256-1115
Pedersen, L., Stang, J., Ulseth, R.: Load prediction method for heat and electricity demand in buildings for the purpose of planning for mixed energy distribution systems. Energy and Buildings 40(7), 1124–1134 (2008)
Popescu, D., Ungureanu, F., Serban, E.: Simulation of Consumption in District Heating Systems. In: Proceedings of the 1st WSEAS International Conference on Urban Rehabilitation and Sustainability (URES 2008), Bucharest, p. 195 (2008) ISBN 978-960-474-023-9, ISSN 1790-5095
Schellong, W., Hentges, F.: Forecast of the Heat Demand of a District Heating System. In: Proceedings of European Power and Energy Systems, Palma de Malorca, Spain (2007)
Torkar, J., Goricanec, D., Krope, J.: Economical heat production and distribution. In: Proceedings of 3rd IASME/WSEAS Int. Conf. on Heat Transfer, Thermal Engineering & Environment, Corfu, p. 445 (2005) ISBN 960-8457-33-5
Turner, S.D., Dudek, S.M., Ritchie, M.D.: Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 86–97. Springer, Heidelberg (2010)
Vařacha, P.: Impact of Weather Inputs on Heating Plant - Aglomeration Modeling. In: Recent Advances in Neural Networks: Proceedings of the 10th WSEAS International Conference on Neural Networks, Prague, p. 193 (2009) ISBN 978-960-474-065-9, ISSN 1790-5109
Vařacha, P.: Neural network synthesis dealing with classification problem. In: Recent ReSearches in Automatic Control: Proceedings of the 13th WSEAS International Conference on Automatic Control, Modelling & Simulation (ACMOS), Lanzarote, pp. 377–382 (2011) ISBN 978-1-61804-004-6
Vařacha, P., Jašek, R.: ANN synthesis for an agglomeration heating power consumption approximation. In: Recent Researches in Automatic Control: Proceedings of the 13th WSEAS International Conference on Automatic Control, Modelling & Simulation (ACMOS), Lanzarote, pp. 239–244 (2011) ISBN 978-1-61804-004-6
Zelinka, I.: SOMA - Self Organizing Migrating Algoritm. In: Batu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering, ch. 7, p. 33. Springer (2004)
Prechelt, L.: Proben1 - A Set of Neural Network Benchmark Problems and Benchmarking Rules. Universität Karlsruhe, Germany (1994)
Vonk, E., Jain, L.C., Johnson, R.P.: Automatic Generation of Neural Network Architecture Using Evolutionary Computation. In: Advances in Fuzzy Systems – Applications and Theory, vol. 14, pp. 981–982. World Science (1997) ISBN: 981-02-3106-7
Vonk, E., Jain, L.C., Veelenturf, L.P.J., Hibbs, R.: Integrating Evolutionary Computation with Neural Networks. In: Electronic Technology Directions to the Year 2000, Adelaide, Australia, pp. 137–143 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chramcov, B., Vařacha, P. (2013). Usage of the Evolutionary Designed Neural Network for Heat Demand Forecast. In: Zelinka, I., Rössler, O., Snášel, V., Abraham, A., Corchado, E. (eds) Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems. Advances in Intelligent Systems and Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33227-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-33227-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33226-5
Online ISBN: 978-3-642-33227-2
eBook Packages: EngineeringEngineering (R0)