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Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique

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  • Yang, L.
  • Entchev, E.

Abstract

This study investigates the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) technique to predict the performance of a hybrid microgeneration system. The hybrid system consists of an internal combustion engine (1kWe and 3.2kWth) integrated with a high efficiency condensing furnace (16.4kWth). Real life system performance data has been collected during a heating/shoulder season in a controlled field-trial at Canadian Centre for Housing Technologies for total of 26days. Four ANFIS models, were developed, trained and validated with the collected filed-trial performance data sets and applied to predicting the system operating temperatures. The MATLAB® ANFIS models were then interfaced with TRNSYS building and controller modules to establish a whole-system model to predict the hybrid microgeneration unit’s seasonal performance.

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  • Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.
    • Handle: RePEc:eee:appene:v:134:y:2014:i:c:p:197-203
    DOI: 10.1016/j.apenergy.2014.08.022
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    References listed on IDEAS

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    Cited by:

    1. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Prasert Aengchuan & Busaba Phruksaphanrat, 2018. "Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 905-923, April.
    3. Strušnik, Dušan & Marčič, Milan & Golob, Marjan & Hribernik, Aleš & Živić, Marija & Avsec, Jurij, 2016. "Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling," Applied Energy, Elsevier, vol. 173(C), pages 386-405.
    4. Dalibor Petković & Milan Gocic & Slavisa Trajkovic & Miloš Milovančević & Dragoljub Šević, 2017. "Precipitation concentration index management by adaptive neuro-fuzzy methodology," Climatic Change, Springer, vol. 141(4), pages 655-669, April.
    5. Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2017. "Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands," Applied Energy, Elsevier, vol. 190(C), pages 222-231.

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