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

Author

Listed:
  • Eslam Mohammed Abdelkader

    (Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt)

  • Nehal Elshaboury

    (Housing and Building National Research Centre, Egypt)

  • Abobakr Al-Sakkaf

    (Department of Architecture and Environmental Planning, College of Engineering and Petroleum, Yemen)

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.

Suggested Citation

  • Eslam Mohammed Abdelkader & Nehal Elshaboury & Abobakr Al-Sakkaf, 2022. "On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-31, January.
    • Handle: RePEc:igg:jamc00:v:13:y:2022:i:1:p:1-31
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    References listed on IDEAS

    as
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    3. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
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