Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir,Turkey

Abstract

In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot–humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance (R²) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size.

Keywords

Artificial neural network Multiple regression Modelling Indoor temperature and relative humidity

Get full access to this article

View all access options for this article.

References

Kaygusuz

: Energy situation, future developments, energy savings, and energy efficiency in Turkey: Energy Source Part A 1999; 21(5): 405–416.

Yüksel

: Energy production and sustainable energy policies in Turkey: Renew Energy 2010; 35: 1469–1476.

TÜBİTAK: Energy and natural resources panel-additional Reports: EK-A: Turkey, The World and OECD data [Enerji Ve Dogal Kaynaklar Paneli-Rapor Ekleri EK-A: Türkiye, Dünya ve OECD Verileri], 2003.

Hepbasli

Utlu

: Comparison of Turkey’s sectoral energy utilization efficiencies between 1990 and 2000 Part 2: Residential- commercial and transportation sectors: Energy Source Part A 2004; 26(14): 1345–1355.

Holz

Hourigan

Sloop

Monkman

Krarti

: Effects of standard energy conserving measures on thermal comfort: Build Environ 1997; 32(1): 31–43.

Tham

Ullah

: Building energy performance and thermal comfort in Singapore: ASHRAE Trans 1993; 99(1): 308–321.

Dombaycı

ÖA

Gölcü

: Daily means ambient temperature prediction using artificial neural network method: a case study of Turkey: Renew Energy 2009; 34: 1158–1161.

Catalina

Virgone

Blanco

: Development and validation of regression models to predict monthly heating demand for residential buildings: Energy Build 2008; 40: 1825–1832.

Dodier

Henze

: Statistical analysis of neural networks as applied to building energy prediction: Trans ASME 126 2004; 126(1): 592–600.

10.

Thomas

Soleimani-Mohseni

: Artificial neural network models for indoor temperature prediction: investigations in two buildings: Neural Comput Appl 2007; 16: 81–89.

11.

Viljanen

: Prediction of indoor temperature and relative humidity using neural network models: model comparison. Neural Comput Appl 2009; 18: 345–357.

12.

Parishwad

Bhardwaj

Nema

: Prediction of monthly-mean hourly relative humidity, ambient temperature, and wind velocity for India: Renew Energy 1998; 13(3): 363–380.

13.

Mohseni

Thomas

Fahlen

: Estimation of operative temperature in buildings using artificial neural networks: Energy Build 2006; 38: 635–640.

14.

Ruano

Crispim

Conceicao

EZE

Lucio

MMJR

: Predictions of building’s temperature using neural networks models: Energy Build 2006; 38: 682–694.

15.

Yigit

Ertunc

: Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks: Int Commun Heat Mass Transfer 2006; 33: 898–907.

16.

Reginato

Freire

Oliveira

GHC

Mendes

Abadie

: Predicting the temperature profile of indoor buildings by using orthonormal basic functions: Eleventh International IBPSA Conference Glasgow, Scotland, July 27–30, 2009.

17.

Mustafaraj

Chen

Lowry

: Thermal behaviour prediction utilizing artificial neural networks for an open office: Appl Math Model 2010; 34: 3216–3230.

18.

Perez

Capeluto

: Climatic considerations in school building design in the hot–humid climate for reducing energy consumption: Appl Energy 2009; 86: 340–348.

19.

825

: Thermal Insulation in Buildings. Ankara, Turkey, Turkish Standard Institution, 1998.

20.

Haykin

: Neural Networks: A Comprehensive Foundation, 2nd edn. Englewood Cliffs, NJ, Prentice Hall, 1998, pp. 88–90.

21.

Göktepe

Altun

Altintas

Tan

: Shear strength estimation of plastic clays with statistical and neural approaches: Build Environ 2008; 43: 849–860.

22.

Fox

: Applied Regression Analysis, Linear Models, and Related Methods. Thousand Oaks, CA, Sage Publications, 1997, pp. 369–436.

23.

Draper

Smith

: Applied Regression Analysis. New York, John Wiley & Sons, 1998, pp. 505–553.

24.

Givoni

: Man, Climate and Architecture, 2nd edn. London, UK, Applied Science Publishers Ltd., 1976, pp. 50–100.

25.

Everett

: The Cambridge Dictionary of Statistics, 2nd edn. Cambridge, Cambridge University Press, 2002, pp. 102–105.

26.

Gokceoglu

: A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition: Eng Geol 2002; 66: 39–51.