Journal article

An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings

DK Bui, TN Nguyen, TD Ngo, H Nguyen-Xuan

Energy | Elsevier | Published : 2020

Abstract

In this study, a new hybrid model, namely the Electromagnetism-based Firefly Algorithm - Artificial Neural Network (EFA-ANN), is proposed to forecast the energy consumption in buildings. The model is applied to evaluate the heating load (HL) and cooling load (CL) using two given datasets. Each dataset was obtained by monitoring the effect of the façade system and dimensions of the building, respectively, on energy consumption. The performance of EFA-ANN is validated by comparing the obtained results with other methods. It is shown that EFA-ANN provides a faster and more accurate prediction of HL and CL. A sensitivity analysis is performed to identify the impact of each input on the energy pe..

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Funding Acknowledgements

The first author would like to thank the University of Melbourne for offering the Melbourne Research Scholarship. This work was mainly supported by the CRC-P for Advanced Manufacturing of High Performance Building Envelope project, funded by the CRC-P program of the Department of Industry, Innovation and Science, Australia, and the Asia Pacific Research Network for Resilient and Affordable Housing (APRAH) grant, funded by the Australian Academy of Science, Australia. This work was also supported by the ARC Training Centre for Advanced Manufacturing of Prefabricated Housing (CAMP.H) at the University of Melbourne.