Power Consumption Modeling for Indoor Environment Using Artificial Neural Network

Article Preview

Abstract:

In this paper, a new Artificial Neural Network (ANN) model that relates human comfort and electrical power consumption of a building with temperature, illumination and carbon dioxide (CO2) level inside the building is developed. The model has been developed using samples of simulated data representing the indoor environment variables. Results have shown that neural network with 14 hidden layer neurons produces outputs that is closest to the actual system outputs.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

221-225

Citation:

Online since:

April 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Kolokotsa, D. et al (2002), Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks, Engineering Applications of Artificial Intelligence, 15(5): 417-428.

DOI: 10.1016/s0952-1976(02)00090-8

Google Scholar

[2] Rui Yang and Lingfeng Wang (2010), Multi-objective optimization for decision-making of energy and comfort management in building automation and control, Sustainable Cities and Society, 2(1): 1– 7.

DOI: 10.1016/j.scs.2011.09.001

Google Scholar

[3] Alonso, J. M et al (1998).

Google Scholar

[4] Fanger, P.O. (1970), Thermal Comfort Analysis and Applications in Environmental Engineering. New York: McGraw-Hill.

Google Scholar

[5] Ari, S. et al (2006), Fuzzy Logic and Neural Network Approximation to Indoor Comfort and Energy Optimization, 2006 Annual meeting of the North American Fuzzy Information Processing Society, 3-6 June, Montreal: IEEE, 692 – 695.

DOI: 10.1109/nafips.2006.365493

Google Scholar

[6] Ma Bingxin et al (2011), Experimental Design and the GA-BP Prediction of Human Thermal Comfort Index, 2011 Seventh International Conference onNatural Computation (ICNC), 26-28 July, Shanghai: IEEE, 771-775.

DOI: 10.1109/icnc.2011.6022146

Google Scholar

[7] Baker, N. et al (1993), Daylight in Architecture, a European Reference Handbook, UK: James & James.

Google Scholar

[8] Hui Xie et al (2009), Prediction of Indoor Air Quality Using Artificial Neural Networks, 2009 Fifth International Conference on Natural Computation, 14-16 August, Tianjin: IEEE, 414 – 418.

DOI: 10.1109/icnc.2009.502

Google Scholar