PREDICTION MODEL OF PUBLIC HOUSES’ HEATING SYSTEMS: A COMPARISON OF SUPPORT VECTOR MACHINE METHOD AND RANDOM FOREST METHOD

Andrii Perekrest

pksg13@gmail.com
Kremenchuk Mykhailo Ostrohradskyi National University (Ukraine)
http://orcid.org/0000-0002-7728-9020

Vladimir Chenchevoi


Kremenchuk Mykhailo Ostrohradskyi National University (Ukraine)
http://orcid.org/0000-0002-6478-3767

Olga Chencheva


Kremenchuk Mykhailo Ostrohradskyi National University (Ukraine)
http://orcid.org/0000-0002-5691-7884

Alexandr Kovalenko


Cherkasy State Technological University (Ukraine)
http://orcid.org/0000-0002-5073-3507

Mykhailo Kushch-Zhyrko


Kremenchuk Mykhailo Ostrohradskyi National University (Ukraine)
http://orcid.org/0000-0001-9622-9114

Aliya Kalizhanova


University of Power Engineering and Telecommunications; Institute of Information and Computational Technologies MES CS RK (Kazakhstan)
http://orcid.org/0000-0002-5979-9756

Yedilkhan Amirgaliyev


Institute of Information and Computational Technologies MES CS RK (Kazakhstan)
http://orcid.org/0000-0002-6528-0619

Abstract

Data analysis and predicting play an important role in managing heat-supplying systems. Applying the models of predicting the systems’ parameters is possible for qualitative management, accepting appropriate decisions relating control that will be aimed at increasing energy efficiency and decreasing the amount of the consumed power source, diagnosing and defining non-typical processes in the functioning of the systems. The article deals with comparing two methods of ma-chine learning: random forest (RF) and support vector machine (SVM) for predicting the temperature of the heat-carrying agent in the heating system based on the data of electronic weather-dependent controller. The authors use the following parameters to compare the models: accuracy, source cost and the opportunity to interpret the results and non-obvious interrelations. The time spent for defining the optimal hyperparameters and conducting the SVM model training is deter-mined to exceed significantly the data of the RF parameter despite the close meanings of the root mean square error (RMSE). The change from 15-min data to once-a-minute ones is done to improve the RF model accuracy. RMSE of the RF model on the test data equals 0.41°С. The article studies the importance of the contribution of variables to the prediction accuracy.


Keywords:

building heat supply, random forest, support vector machine

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Published
2022-09-30

Cited by

Perekrest, A., Chenchevoi, V., Chencheva, O., Kovalenko, A., Kushch-Zhyrko, M., Kalizhanova, A., & Amirgaliyev, Y. (2022). PREDICTION MODEL OF PUBLIC HOUSES’ HEATING SYSTEMS: A COMPARISON OF SUPPORT VECTOR MACHINE METHOD AND RANDOM FOREST METHOD. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(3), 34–39. https://doi.org/10.35784/iapgos.3032

Authors

Andrii Perekrest 
pksg13@gmail.com
Kremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0002-7728-9020

Authors

Vladimir Chenchevoi 

Kremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0002-6478-3767

Authors

Olga Chencheva 

Kremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0002-5691-7884

Authors

Alexandr Kovalenko 

Cherkasy State Technological University Ukraine
http://orcid.org/0000-0002-5073-3507

Authors

Mykhailo Kushch-Zhyrko 

Kremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0001-9622-9114

Authors

Aliya Kalizhanova 

University of Power Engineering and Telecommunications; Institute of Information and Computational Technologies MES CS RK Kazakhstan
http://orcid.org/0000-0002-5979-9756

Authors

Yedilkhan Amirgaliyev 

Institute of Information and Computational Technologies MES CS RK Kazakhstan
http://orcid.org/0000-0002-6528-0619

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