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

L. Yang and E. Entchev

Applied Energy, 2014, vol. 134, issue C, 197-203

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.

Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS); Hybrid microgeneration system; Internal combustion engine; High efficiency condensing furnace; Seasonal performance (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:134:y:2014:i:c:p:197-203

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DOI: 10.1016/j.apenergy.2014.08.022

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