Scholarly record
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
Abstract
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.
Publication Impact Profile
Publication details
References0
Structured references will appear here after the reference import pass. The count is preserved now so the scholarly record is not incomplete.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.