Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor vs. GA-BP Based Prediction Model in Indoor Temperature Predicting

Abstract:

The previous research on temperature prediction presented different approaches which are physical-rule based adaptive neuro-fuzzy inferential sensor (ANFIS) model and GA-BP (genetic algorithm back propagation) based model to estimate the average indoor temperature in the building environment. Their good prediction performances improved energy efficiency of district heating system and indoor comfort ratio. However, either of these two models has its drawback in a certain condition. In this paper, the two prediction models are reviewed and evaluated by three performance measures (RMSE, RMS, and R2). Their limitations are discussed and potential solution is proposed.

• Periodical: Advanced Materials Research (Volumes 594-597)
• Pages: 2179-2185
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.594-597.2179
• Online since: November 2012
• Authors: Liang Huang, Zai Yi Liao
• Keywords: Adaptive Neuro-Fuzzy Inferential Sensor, District Heating System, Energy Efficiency, GA-BP Based Prediction Model, Heat Exchanger Control

References

[1] Liang Huang, Zaiyi Liao. A neuro-fuzzy based adaptive set-point heating exchanger control scheme in district heating system. The 7th International Symposium on Heating, Vetilation and Air Conditioning, 2011.

[2] Liao Z, Dexter AL, An inferential control scheme for optimizing the operation of boilers in multi-zone heating systems. Building and Environment (2004); 39(9):1009-18.

DOI: 10.1191/0143624403bt075oa

[3] S. Jassar, Z. Liao and L. Zhao, Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems, Building and Environment, (2009);44: 1609-1616.

DOI: 10.1016/j.buildenv.2008.10.002

[4] Liang Huang, Zaiyi Liao, Hua Ge, & Lian Zhao, Physical rules based adaptive neuro-fuzzy inferential sensor model for predicting the indoor temperature in heating system. Advantage Material Research, ICEEP (2012): Electrical Power & Energy Systems; ISSN: 1662-8985

DOI: 10.4028/www.scientific.net/amr.516-517.370

[5] Abdennour A. Long horizon neuro-fuzzy predictor for MPEG video traffic. J King Saud Univ (2005); 18:161–80.

DOI: 10.1016/s1018-3639(18)30827-4

[6] Alotaibi FD, Ali AA, TETRA outdoor large-scale received signal prediction model in Riyadh city-Saudi Arabia. In: Proceedings of the IEEE wireless and microwave technology conference (WAMICON 2006), (2006):1–5.

DOI: 10.1109/wamicon.2006.351966

[7] Kreider JF 1995. Neural networks applied to building energy studies. In: Bloem H, editor. Workshop on parameter identification. Ispra: JRC Ispra, (1995): 243–51.

[8] Hepeworth SJ, Arthur AL. Adaptive neural control with stable learning. Mathematics and Computers in Simulation, 2000; 41:39–51.

[9] Soleimani-Moheseni M, Thomas B, Fahlen Per. Estimation of operative temperature in buildings using artificial neural networks. Energy and Buildings, (2006); 38:635–40.

DOI: 10.1016/j.enbuild.2005.10.004

[10] Hollan, J.H., Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, Michigan, (1975).

[11] Lienhard, John H., IV; Lienhard, John H., V. A Heat Transfer Textbook (3rd ed.). Cambridge, Massachusetts: Phlogiston Press. (2008); ISBN 9780971383531.

[12] Goldberg, D., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, (1989).

[13] Hiroaki Kitano, Empirical studies on the speed of convergence of neural network training using genetic algorithms, AAAI'90 Proceedings of the eight national conference of artificial intelligence. (1990):2.

[14] BRE, ICITE. Controller efficiency improvement for commercial and industrial gas and oil fired boilers, A CRAFT project, Contract JOE-CT98–7010; (1999–2001).