dc.creator | Sretenović, Aleksandra | |
dc.creator | Jovanović, Radiša | |
dc.creator | Novaković, Vojislav M. | |
dc.creator | Nord, Nataša M. | |
dc.creator | Živković, Branislav | |
dc.date.accessioned | 2023-02-28T07:07:02Z | |
dc.date.available | 2023-02-28T07:07:02Z | |
dc.date.issued | 2017 | |
dc.identifier.isbn | 978-86-7912-657-3 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/4713 | |
dc.description.abstract | In this paper, feedforward neural network, one of the most widely used
artificial intelligence methods, was proposed for the prediction of hourly
heating energy use of one university campus. Two different approaches were
presented: network that provides one output (heating energy use for selected
hour) and network with 24 outputs (daily profile of heating energy use). The
proposed models were trained and tested using real measured hourly energy
use and meteorological data. It has been shown that both models can be used
for the prediction with satisfying accuracy. This kind of prediction can be
used for calculating accurate energy bills, which is very useful considering
that the significant part of the campus is being leased. Estimating energy use
for different weather conditions can help in energy planning. | sr |
dc.language.iso | en | sr |
dc.rights | openAccess | sr |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | International Scientific Conference on Information Technology and Data Related Research, SINTEZA | sr |
dc.subject | neural networks | sr |
dc.subject | building energy use prediction | sr |
dc.title | Prediction of hourly heating energy use for HVAC using feedforward neural networks | sr |
dc.type | conferenceObject | sr |
dc.rights.license | BY-NC-ND | sr |
dc.citation.epage | 301 | |
dc.citation.rank | M31 | |
dc.citation.spage | 297 | |
dc.identifier.doi | 10.15308/Sinteza-2017-297-301 | |
dc.identifier.fulltext | http://machinery.mas.bg.ac.rs/bitstream/id/11329/297-301.pdf | |
dc.type.version | publishedVersion | sr |