The role of data sample size and dimensionality in neural networkbased forecasting of building heating related variables
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F16%3A00239524" target="_blank" >RIV/68407700:21730/16:00239524 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0378778815304217" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0378778815304217</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.enbuild.2015.11.056" target="_blank" >10.1016/j.enbuild.2015.11.056</a>
Alternative languages
Result language
angličtina
Original language name
The role of data sample size and dimensionality in neural networkbased forecasting of building heating related variables
Original language description
Energy consumed in buildings represents a challenge in the context of reduction of greenhouse gases emission. For this reason and due to the growing interest in operative costs reduction the energy used by buildings (tertiary and privates) for heating, ventilating, and air conditioning (HVAC) is even more investigated. Due to the nature of the energy consumption profile a predictive optimization method is one of the solution the scientific literature spreads even more. However optimization techniques need a good and reliable prediction of the variables of interest over a time horizon. This work focuses on methods to obtain a robust and reliable predictor based on Artificial Neural Networks. For the optimization purposes the neural model predicts total heating energy consumption (gas), internal air temperature and aggregated thermal discomfort 12 hours ahead.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GP13-21696P" target="_blank" >GP13-21696P: Feature selection for temporal context aware models of multivariate time series</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy and Buildings
ISSN
0378-7788
e-ISSN
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Volume of the periodical
111
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
12
Pages from-to
299-310
UT code for WoS article
000369191100028
EID of the result in the Scopus database
2-s2.0-84948953396