Primena mašinskog učenja u predviđanju energetskih performansa toplifikacionog sistema
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Apstrakt
U ovom radu istrazÌŒivane su mogucÌnosti predviđanja energetskih performansi toplifikacionog sistema MasÌŒinskog fakulteta u NisÌŒu primenom nadgledanog masÌŒinskog ucÌŒenja. Kontrola rada top- lifikacionog sistema MasÌŒinskog fakulteta u NisÌŒu se odvija automatski. PracÌenje energetskih perfor- mansi obavlja se pomocÌu SCADA sistema ali odluke o radu, koje se ticÌŒu unapređenja sistema u smislu usÌŒtede energije a samim tim i smanjenja trosÌŒkova, donosi operater toplane. PredlozÌŒena predviđanja u radu zasnivaju se na primeni vesÌŒtacÌŒkih neuronskih mrezÌŒa nad skupom energetskih indikatora preuzetih iz SCADA sistema toplane MasÌŒinskog fakulteta u NisÌŒu. Predstavljeni su rezultati predikcije utrosÌŒene toplotne energije u posmatranom vremenskom intervalu koji obuhvata period od 15 dana, dobijeni primenom razlicÌŒitih alogiritama neuronskih mrezÌŒa u softverskom alatu Matlab a u cilju da se pokazÌŒe da su korisÌŒcÌeni algoritmi neuronskih mrezÌŒa sposobni da, sa dovoljnom preciznosÌŒcÌu, razumeju kompleksan sistem daljinskog grejanja kako bi se moglo omogucÌiti korisÌŒcÌenje nekih od prikazanih modela predviđanja za detekciju anomalija u radu toplifikacionog sistema MasÌŒinskog fakulteta u NisÌŒu.
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Reference
[2] Milan Zdravković, Ivan Ćirić, Marko Ignjatović, Towards explainable AI-assisted operations
in district heating systems, IFAC PapersOnLine 54-1 (2021) 390 – 395, Niš, 2021.
[3] Miloš B. Simonović, Vlastimir D. Nikolić, Emina P. Petrović and Ivan T. Ćirić, Heat load prediction of small district heating system using artificial neural network, Thermal Science, Vol.
20 (2016), Suppl. 5, pp. S1355-S1365
[4] Ivan Ćirić, Marko Ignjatović, Mirko Stojiljković, Dušan Stojiljković, Milan Gocić, Milica
Ćirić, Intelligent Heat Demand prediction for Advanced District Heat Plant Control, 10th Inter-
national Conference on Information Society and Technology, Belgrade, Serbia, 2020.
[5] Simonović, M. B., Artificial Neural Network Application for Short-Term Prediction and Anal-
ysis of District Heating Systems, Ph.D. thesis, University of Niš, Niš, Serbia, 2016.
[6] Davide Quaggiotto, Jacopo Vivian, Angelo Zarrella, Management of a district heating net- work using model predictive control with and without thermal storage, Optimization and Engi-
neering, Vol. 22 (2021), Issue 4, pp. 1901 – 1931.
[7] Madhuranthakam, C., Nigam, A., Pathak, P., A review of PID control, tuning methods and
applications, International Journal of Dynamics and Control, Vol. 9 (2021), Issue 1, pp. 88 –
105.
[8] Bukhari, A., S. H. Yusoff, M. S. F. M. Yunus, N. S. I. Razali, Virtual Power Plant Management
Using PID Controller, Journal of Renewable Energy and Power Systems, Vol. 9 (2021), Issue,
pp. 818 – 827.
[9] Maryniak, A., M. BanaÅ›, P. Michalak, J. Szymiczek, Forecasting of Daily Heat Production in
a District Heating Plant Using a Neural Network, Energies, Vol. 17 (2024), issue 17, pp. 4369.
[10] Hae-Won Uh, Lucia Klarić, Ivo Ugrina, Gordan Lauc, Age K. Smilde and Jeanine J. Houwing-Duistermaat, Choosing proper normalization is essential for discovery of sparse gly-
can biomarker, Molecular Omics, Vol. 16 (2020)
[11] B. Chramcov, Heat demand forecasting for concrete district heating system, Int. J. Math. Model.
Methods Appl. Sci., Vol. 4, no. 4, pp. 231 – 239, 2010.
[12] Petrović, M., Basics of Artificial Neural Networks and the Importance of Their Application,
Proceedings of the Faculty of Civil Engineering, Vol. 20 (2011), pp. 47 – 55.
[13] Äozić, D., The Use of Artificial Neural Networks for Predicting Behavior and Managing Com-
plex Energy Systems, Ph. D. thesis, University of Novi Sad, Novi Sad, Serbia, 2020.
[14] J. Xie, H. Li, Z. Ma, Q. Sun, F. Wallin, Z. Si, J. Guo, Analysis of Key Factors in Heat Demand Prediction with Neural Networks, Proceedings of 8th International Conference on Applied En-
ergy, ICAE2016, 8-11 October 2016, Beijing, China, pp. 2965-2970
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