Influence of the time step in ann modelling of thermal stratification of solar storage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the present work an artificial neural network (ANN) model is introduced which was elaborated for modelling the layer temperatures in a storage tank of a solar thermal system. The model calculates the temperatures of 8 equal layers of the storage tank in several time interval from the average time interval data of the solar radiation, the water consumption, the ambient temperature, the mass flow rate of collector loop and the temperature of the layers in the previous time-step. The used time intervals are one hour, 30, 10, 5, 2 and 1 minutes. The introduced ANN model is convenient for describing the system in every case, and the identified models give acceptable results inside the training interval. The average deviation was 0.53°C during the training and 0.76°C during the validation in case of hourly data and these data were 0.07°C and 0.08 in case of 1 minute time interval. The optimal time interval was found at 5 minutes.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume17
Edition1 PART 1
DOIs
Publication statusPublished - 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: Jul 6 2008Jul 11 2008

Other

Other17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of
CitySeoul
Period7/6/087/11/08

Fingerprint

Thermal stratification
Neural networks
Temperature
Solar radiation
Flow rate
Water

Keywords

  • Agricultural solar energy use
  • AI in agriculture
  • Modeling and control of agriculture

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Influence of the time step in ann modelling of thermal stratification of solar storage. / Farkas, I.

IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 17 1 PART 1. ed. 2008.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Farkas, I 2008, Influence of the time step in ann modelling of thermal stratification of solar storage. in IFAC Proceedings Volumes (IFAC-PapersOnline). 1 PART 1 edn, vol. 17, 17th World Congress, International Federation of Automatic Control, IFAC, Seoul, Korea, Republic of, 7/6/08. https://doi.org/10.3182/20080706-5-KR-1001.2907
Farkas, I. / Influence of the time step in ann modelling of thermal stratification of solar storage. IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 17 1 PART 1. ed. 2008.
@inproceedings{22bf0ff7ae4745e2baf80447f8b71bc1,
title = "Influence of the time step in ann modelling of thermal stratification of solar storage",
abstract = "In the present work an artificial neural network (ANN) model is introduced which was elaborated for modelling the layer temperatures in a storage tank of a solar thermal system. The model calculates the temperatures of 8 equal layers of the storage tank in several time interval from the average time interval data of the solar radiation, the water consumption, the ambient temperature, the mass flow rate of collector loop and the temperature of the layers in the previous time-step. The used time intervals are one hour, 30, 10, 5, 2 and 1 minutes. The introduced ANN model is convenient for describing the system in every case, and the identified models give acceptable results inside the training interval. The average deviation was 0.53°C during the training and 0.76°C during the validation in case of hourly data and these data were 0.07°C and 0.08 in case of 1 minute time interval. The optimal time interval was found at 5 minutes.",
keywords = "Agricultural solar energy use, AI in agriculture, Modeling and control of agriculture",
author = "I. Farkas",
year = "2008",
doi = "10.3182/20080706-5-KR-1001.2907",
language = "English",
isbn = "9783902661005",
volume = "17",
booktitle = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
edition = "1 PART 1",

}

TY - GEN

T1 - Influence of the time step in ann modelling of thermal stratification of solar storage

AU - Farkas, I.

PY - 2008

Y1 - 2008

N2 - In the present work an artificial neural network (ANN) model is introduced which was elaborated for modelling the layer temperatures in a storage tank of a solar thermal system. The model calculates the temperatures of 8 equal layers of the storage tank in several time interval from the average time interval data of the solar radiation, the water consumption, the ambient temperature, the mass flow rate of collector loop and the temperature of the layers in the previous time-step. The used time intervals are one hour, 30, 10, 5, 2 and 1 minutes. The introduced ANN model is convenient for describing the system in every case, and the identified models give acceptable results inside the training interval. The average deviation was 0.53°C during the training and 0.76°C during the validation in case of hourly data and these data were 0.07°C and 0.08 in case of 1 minute time interval. The optimal time interval was found at 5 minutes.

AB - In the present work an artificial neural network (ANN) model is introduced which was elaborated for modelling the layer temperatures in a storage tank of a solar thermal system. The model calculates the temperatures of 8 equal layers of the storage tank in several time interval from the average time interval data of the solar radiation, the water consumption, the ambient temperature, the mass flow rate of collector loop and the temperature of the layers in the previous time-step. The used time intervals are one hour, 30, 10, 5, 2 and 1 minutes. The introduced ANN model is convenient for describing the system in every case, and the identified models give acceptable results inside the training interval. The average deviation was 0.53°C during the training and 0.76°C during the validation in case of hourly data and these data were 0.07°C and 0.08 in case of 1 minute time interval. The optimal time interval was found at 5 minutes.

KW - Agricultural solar energy use

KW - AI in agriculture

KW - Modeling and control of agriculture

UR - http://www.scopus.com/inward/record.url?scp=79961019976&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79961019976&partnerID=8YFLogxK

U2 - 10.3182/20080706-5-KR-1001.2907

DO - 10.3182/20080706-5-KR-1001.2907

M3 - Conference contribution

SN - 9783902661005

VL - 17

BT - IFAC Proceedings Volumes (IFAC-PapersOnline)

ER -