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• W2026040110 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 (1 kWe and 3.2 kWth) integrated with a high efficiency condensing furnace (16.4 kWth). 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 26 days. 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. The validation results confirm the applicability of the developed ANFIS models for predicting system operating temperatures over a wide range of operational conditions. The study demonstrates that the integrated system model is capable of predicting the system’s seasonal performance with a high degree of accuracy and minimum time demand, and can effectively replace the costly and time consuming real-life experiments and trails." @default.
• W2026040110 created "2016-06-24" @default.
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• W2026040110 date "2014-12-01" @default.
• W2026040110 modified "2023-10-13" @default.
• W2026040110 title "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique" @default.
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• W2026040110 doi "https://doi.org/10.1016/j.apenergy.2014.08.022" @default.
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