INPUT VECTOR IMPACT ON SHORT-TERM HEAT LOAD PREDICTION OF SMALL DISTRICT HEATING SYSTEM

Miloš Simonović, Vlastimir Nikolić, Emina Petrović

DOI Number
-
First page
95
Last page
103

Abstract


Short-term load prediction is very important for advanced decision making in district heating systems. The idea is to achieve quality prediction for a short period in order to reduce the consumption of heat energy production and increased coefficient of exploitation of equipment. The common thing for each way of prediction is usage of historical data for certain last period which makes possible development of many methodologies for adequate prediction and control. In this paper, application of feedforward artificial neural network for short-term load prediction for period of 1, 3 and 7 days, of one small district heating system, is presented. Three different input vectors are implemented and their impact on quality of prediction discussed. The simulation results are compared and detailed analysis is done where operation in transient regime is of special importance. Satisfied prediction average error is obtained.

Keywords

: short-term load prediction, feedforward artificial neural networks, small district heating system, energy efficiency, heat load, heat demand

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References


S. Karatasou, M. Santamouris, V. Geros, "Modelling and predicting building’s energy use with artificial neural networks: Methods and results", Energy and Building, vol. 38, pp. 949–958, 2006. [Online]. Available: http://dx.doi.org/10.1016/j.enbuild.2005.11.005

M.Y. Rafiq, G. Bugmann, D. J. Easterbrook, "Neural network design for engineering applications", Computers and Structures, pp. 1541–1552, 2001. [Online]. Available: http://dx.doi.org/10.1016/S0045-7949(01)00039-6

T.C. Park, U. S. Kim, L. H. Kim, B.W. Jo, Y.K. Yeo, "Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems", Korean J. Chem. Eng., vol. 27, no. 4, pp. 1063–1071, 2010. [Online]. Available: http://dx.doi.org/

1007/s11814-010-0220-9

G. Box, G. Jenkins, G. Reinsel, Time series analysis: forecasting and control, Wiley Series in Probability and Statistics, 4th edition, 2008.

E. Dotzauer, "Simple model for prediction of loads in district-heating systems", Applied Energy, vol. 73, pp. 277–284, 2002. [Online]. Available: http://dx.doi.org/10.1016/S0306-2619(02)00078-8

A. J. Al-Shareef, E. A. Mohamed, E. Al-Judaibi, "One-hour ahead load forecasting using artificial neural network for the western area of Saudi Arabia", International J. Electr. Systems Sci. Eng, pp. 219–224, 2006. [Online]. Available: http://www.waset.org/publications/3362

D. Niebur et al. "Artificial neural networks for Power Systems", CIGRE TF38.06.06 Report, ELECTRA, pp. 77-101, 1995.

J.L. Elman, "Distributed representations, simple recurrent networks, and grammatical structure", Mach Leurn , vol. 7, no. 2-3, pp 95–126, 1991. . [Online]. Available: http://dx.doi.org/10.1007/BF00114844

W.S. Sarle, "Stopped training and other remedies for overfitting", In: Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics. Convention Center and Vista Hotel, Pittsburgh, PA; vol. 27, pp. 352–360, 1995.

R.Z. Jovanovic, A.A. Sretenovic, B.D. Zivkovic, "Ensemble of various neural networks for prediction of heating energy consumption", Energy and Buildings 94, pp. 189–199, 2015. [Online]. Available: http://dx.doi.org/10.1016/j.enbuild.2015.02.052

K.M. Powell, A. Sriprasad, W.J. Cole, T. F. Edgar, "Heating, cooling, and electrical load forecasting for a large-scale district energy system", Energy, vol. 74, pp. 877–885, 2014. [Online]. Available: http://dx.doi.org/10.1016/j.energy.2014.07.064


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