On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

Eslam Mohammed Abdelkader, Nehal Elshaboury, Abobakr Al-Sakkaf

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)13(1)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.296262

MLA

Abdelkader, Eslam Mohammed, et al. "On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings." IJAMC vol.13, no.1 2022: pp.1-31. http://doi.org/10.4018/IJAMC.296262

APA

Abdelkader, E. M., Elshaboury, N., & Al-Sakkaf, A. (2022). On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-31. http://doi.org/10.4018/IJAMC.296262

Chicago

Abdelkader, Eslam Mohammed, Nehal Elshaboury, and Abobakr Al-Sakkaf. "On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-31. http://doi.org/10.4018/IJAMC.296262

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Abstract

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, 10 meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. An integrative ranking of the models is achieved using average ranking algorithm.