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
Ahmad M. V. et al.: Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression. Energy 164, 2018, 465–474.
DOI: https://doi.org/10.1016/j.energy.2018.08.207
Google Scholar
Ahmad, M. V. et al.: Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees. Journal of Cleaner Production 203, 2018, 810–821.
DOI: https://doi.org/10.1016/j.jclepro.2018.08.207
Google Scholar
Ahmad M. W. et al.: Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings 147, 2017, 77–89.
DOI: https://doi.org/10.1016/j.enbuild.2017.04.038
Google Scholar
Azarov A. D. et al.: Class of numerical systems for pipe-line bit sequential development of multiple optoelectronic data streams. Proc. SPIE 4425, 2001, 406-409.
DOI: https://doi.org/10.1117/12.429761
Google Scholar
Azarov A.D. et al.: Static and dynamic characteristics of the self-calibrating multibit ADC analog components. Proc. SPIE 8698, 2012, 86980N.
DOI: https://doi.org/10.1117/12.2019737
Google Scholar
Breiman L.: Out-of-bag estimation. Tech. rep. University of California, 1996 [https://www.stat.berkeley.edu/~breiman/OOBestimation.pdf].
Google Scholar
Breiman L.: Random Forests. Machine Learning 45(1), 2001, 5–32.
DOI: https://doi.org/10.1023/A:1010933404324
Google Scholar
Dong B. et al.: Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 37(5), 2005, 545–553.
DOI: https://doi.org/10.1016/j.enbuild.2004.09.009
Google Scholar
Esen H. et al.: Modelling of a new solar air heater through least-squares support vector machines. Expert Systems with Applications 36(7), 2009, 10673–1068.
DOI: https://doi.org/10.1016/j.eswa.2009.02.045
Google Scholar
Geng Y. et al.: Building energy performance diagnosis using energy bills and weather data. Energy and Buildings 172, 2018, 181–191.
DOI: https://doi.org/10.1016/j.enbuild.2018.04.047
Google Scholar
Kaczmarek C. et al.: Measurement of pressure sensitivity of modal birefringence of birefringent optical fibers using a Sagnac interferometer. Optica Applicata 45(1), 2015, 5–14.
DOI: https://doi.org/10.1109/ICSENS.2015.7370173
Google Scholar
Kukharchuk V. V. et al.: Method of magneto-elastic control of mechanic rigidity in assemblies of hydropower units. Proc. SPIE 10445, 2017, 104456A.
DOI: https://doi.org/10.1117/12.2280974
Google Scholar
Kukharchuk V. V. et al.: Noncontact method of temperature measurement based on the phenomenon of the luminophor temperature decreasing. Proc. SPIE 10031, 2016, 100312F.
DOI: https://doi.org/10.1117/12.2249358
Google Scholar
Kukharchuk V. V. et al.: Discrete wavelet transformation in spectral analysis of vibration processes at hydropower units. Przegląd Elektrotechniczny 93(5), 2017, 65–68.
Google Scholar
Kvyetnyy R. et al.: Blur recognition using second fundamental form of image surface. Proc. SPIE 9816, 2015, 98161A.
DOI: https://doi.org/10.1117/12.2229103
Google Scholar
Kvyetnyy R. et al.: Method of image texture seg-mentation using Laws' energy measures. Proc. SPIE 10445, 2017, 1044561.
DOI: https://doi.org/10.1117/12.2280891
Google Scholar
Kvyetnyy R. et al.: Modification of fractal coding algorithm by a combination of modern technologies and parallel computations. Proc. SPIE 9816, 2015, 98161R.
DOI: https://doi.org/10.1117/12.2229009
Google Scholar
Osadchuk A. et al.: Pressure transducer of the on the basis of reactive properties of transistor structure with negative resistance. Proc. SPIE 9816, 2015, 98161C.
DOI: https://doi.org/10.1117/12.2229211
Google Scholar
Osadchuk O. et al.: The Generator of Superhigh Frequencies on the Basis Silicon Germanium Heterojunction Bipolar Transistors. 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016, 336 – 338.
DOI: https://doi.org/10.1109/TCSET.2016.7452051
Google Scholar
Paluszyska A.: Structure mining and knowledge extraction from random forest with applications to The Cancer Genome Atlas project. 2017. [https://rawgit.com/geneticsMiNIng/BlackBoxOpener/master/randomForestExplainer_Master_thesis.pdf].
Google Scholar
Parfenenko Yu. V. et al.: Prediction the heat consumption of social and public sector buildings using neural networks. Radio Electronics, Computer Science, Control 2, 2015, 41–46.
DOI: https://doi.org/10.15588/1607-3274-2015-2-5
Google Scholar
Perekrest A. et al.: Key Performance Indicators Assessment Methodology Principles Adaptation for Heating Systems of Administrative and Residential Buildings. IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP), 2020, 1–4.
DOI: https://doi.org/10.1109/PAEP49887.2020.9240784
Google Scholar
Perekrest A. et al.: Complex information and technical solutions for energy management of municipal energetics. Proc. SPIE 10445, 2017, 1044567.
DOI: https://doi.org/10.1117/12.2280962
Google Scholar
Ruiz L. G. B. et al.: Energy consumption forecasting based on Elman neural networks with evolutive optimization. Expert Systems with Applications 92, 2018, 380–389.
DOI: https://doi.org/10.1016/j.eswa.2017.09.059
Google Scholar
Smolarz A. et al.: Fuzzy controller for a lean premixed burner. Przegląd Elektrotechniczny 86(7), 2010, 287–289.
Google Scholar
Vapnik V., Chapelle O.: Bounds on error expectation for suport vector machines. Neural Computation 12 (9), 2000, 2013–2036.
DOI: https://doi.org/10.1162/089976600300015042
Google Scholar
Vedmitskyi Y. G. et al.: New non-system physical quantities for vibration monitoring of transient processes at hydropower facilities, integral vibratory accelerations. Przegląd Elektrotechniczny 93(3), 2017, 69–72.
DOI: https://doi.org/10.15199/48.2017.03.17
Google Scholar
Wei Y. et al.: A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews 82, 2018, 1027–1047.
DOI: https://doi.org/10.1016/j.rser.2017.09.108
Google Scholar
Wójcik W. et al.: Concept of application of signals from fiber-optic system for flame monitoring to control separate pulverized coal burner. Proc. SPIE 5484, 2004, 427–431.
DOI: https://doi.org/10.1117/12.569041
Google Scholar
Wójcik W. et al.: Vision based monitoring of coal flames source. Przegląd Elektrotechniczny 84(3), 2008, 241–243.
Google Scholar
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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