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Title: DEVELOPMENT OF A METHOD FOR PREDICTING THE HEAT CONSUMPTION OF BUILDINGS WITH REGARD TO THEIR INDIVIDUAL CHARACTERISTICS BASED ON THE USE OF MLP BOOSTING AND LINEAR CLASSIFIERS
DEVELOPMENT OF A METHOD FOR PREDICTING THE HEAT CONSUMPTION OF BUILDINGS WITH REGARD TO THEIR INDIVIDUAL CHARACTERISTICS BASED ON THE USE OF MLP BOOSTING AND LINEAR CLASSIFIERS

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Yu. Koshlich;A. Belousov;D. Bukhanov;A. Grebenik;V. Stan
10.5593/sgem2019/2.1/S07.029
1314-2704
978-619-7408-79-9
English
19
2.1
Informatics
The paper analyzes the existing data mining methods to predict heat consumption in buildings of public sector facilities. The highest accuracy in solving the regression problem was obtained using the ensembles of artificial neural network models of the MLP architecture, gradient boosting models on decision-making trees and linear regression models. The ensembles of the studied methods were used in the development of the analytical subsystem in the energy resource management system of the Belgorod Region. Approbation of the developed subsystem was performed using data from 2018 and 2019. High results of experiments on real data proved the adequacy of the proposed models.
conference
https://www.sgem.org/index.php/jresearch-article?citekey=Koshlich20197217224
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19th International Multidisciplinary Scientific GeoConference SGEM 2019
19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference-SGEM
Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
217-224
30 June - 6 July, 2019
website
cdrom
5351
data mining methods; artificial neural networks; perceptron; gradient boosting; heat consumption prediction.