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Accurate models for heat load prediction are essential to the operation and planning of a utility company. Load prediction helps a heat utility to make important and advanced decisions in district heating systems. As a popular data driven method, artificial neural networks are often used for prediction. The main 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. To improve the short term prediction accuracy, this paper presents a kind of improved artificial neural network model for 1 to 7 days ahead prediction of heat consumption of energy produced in small district heating system. Historical data set of one small district heating system from city of Nis, Serbia, was used. Particle swarm optimization is applied to adjust artificial neural network weights and threshold values. In this paper, application of feed forward artificial neural network for short-term prediction for period of 1, 3, and 7 days, of small district heating system, is presented. Two test data sets were considered with different interruption non-stationary performances. Comparison of prediction accuracy between regular and improved artificial neural network model was done. The comparison results reveal that improved artificial neural network model have better accuracy than that of artificial neural network ones.

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How to Cite
SIMONOVIĆ, Miloš B. et al. HEAT LOAD PREDICTION OF SMALL DISTRICT HEATING SYSTEM USING ARTIFICIAL NEURAL NETWORKS. Thermal Science, [S.l.], v. 20, p. S1355-S1365, feb. 2017. ISSN 2334-7163. Available at: <http://thermal-science.tech/journal/index.php/thsci/article/view/1655>. Date accessed: 14 dec. 2017. doi: https://doi.org/10.2298/TSCI16S5355S.
Received 2017-02-07
Accepted 2017-02-07
Published 2017-02-07


[1] Karatasou, S., et al., Modelling and Predicting Building’s Energy Use with Artificial Neural Networks: Methods and Results, Energy and Building, 38, (2006), 8, pp. 949-958
[2] Wojdyga, K., Predicting Heat Demand for a District Heating Systems. Int. Journal of energy and Power Engineering, 3 (2014), 5 pp. 237-244
[3] Rafiq, M. Y., et al., Neural Network Design for Engineering Applications, Computers and Structures, 79, (2001), 17, pp. 1541-1552
[4] Park, T. C., et al., Heat Consumption Forecasting Using Partial Least Squares, Artificial Neural Network and Support Vector Regression Techniques in District Heating Systems, Korean J. Chem. Eng., 27 (2010), 4, pp. 1063-1071
[5] Box, G., et al., Time Series Analysis: Forecasting and Control, Wiley Series in Probability and Statistics, 4th ed., New York, USA, 2008
[6] Ilić, S. A. et al.: Hybrid Artificial Neural Network System for Short-Term Load Forecasting, Thermal Science, 16 (2102), Suppl. 1, pp. S215-S224
[7] Dotzauer, E., Simple Model for Prediction of Loads in District-heating Systems, Applied Energy, 73 (2002), 3, pp. 277-284
[8] Sarle, W. S., Stopped Training and Other Remedies for Overfitting, Proceedings, 27th Symposium on the Interface of Computing Science and Statistics, Pittsburgh, Penn., USA, Vol. 27, 1995, pp. 352-360
[9] Jovanović, R., Sretenović, A., Various Multistage Ensembles for Prediction of Heating Energy Consumption, Thermal Science, 36 (2015), 2, pp. 119-132
[10] Powell, K. M., et al., Heating, Cooling, and Electrical Load Forecasting for a Large-Scale District Energy System, Energy, 74 (2014), Sep., pp. 877-885
[11] Kennedy, J., Eberhart, R. C., Particle Swarm Optimization. Proceedings, IEEE International Conference on Neural Networks, Perth, Australia, Vol IV (1995), pp. 1942-1948
[12] Hill, N. M., et al., Can Neural Networks be Applied to Time Series Forecasting and Learn Seasonal Patterns: An Empirical Investigation, Proceedings, 27th Annual Hawaii International Conference on System Sciences, Hi., USA, 1994, pp. 649-655

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