PREDICTION MODEL OF PUBLIC HOUSES’ HEATING SYSTEMS: A COMPARISON OF SUPPORT VECTOR MACHINE METHOD AND RANDOM FOREST METHOD
Andrii Perekrest
pksg13@gmail.comKremenchuk 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 machineReferences
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Authors
Andrii Perekrestpksg13@gmail.com
Kremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0002-7728-9020
Authors
Vladimir ChenchevoiKremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0002-6478-3767
Authors
Olga ChenchevaKremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0002-5691-7884
Authors
Alexandr KovalenkoCherkasy State Technological University Ukraine
http://orcid.org/0000-0002-5073-3507
Authors
Mykhailo Kushch-ZhyrkoKremenchuk Mykhailo Ostrohradskyi National University Ukraine
http://orcid.org/0000-0001-9622-9114
Authors
Aliya KalizhanovaUniversity of Power Engineering and Telecommunications; Institute of Information and Computational Technologies MES CS RK Kazakhstan
http://orcid.org/0000-0002-5979-9756
Authors
Yedilkhan AmirgaliyevInstitute of Information and Computational Technologies MES CS RK Kazakhstan
http://orcid.org/0000-0002-6528-0619
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