The role of data sample size and dimensionality in neural networkbased forecasting of building heating related variables

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

The role of data sample size and dimensionality in neural networkbased forecasting of building heating related variables

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

The role of data sample size and dimensionality in neural networkbased forecasting of building heating related variables

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/GP13-21696P" target="_blank" >GP13-21696P: Selekce příznaků pro modely vícedimenzionálních časových řad uvažující časový kontext</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy and Buildings

ISSN

0378-7788

e-ISSN

—

Svazek periodika

111

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

299-310

Kód UT WoS článku

000369191100028

EID výsledku v databázi Scopus

2-s2.0-84948953396