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

Author:

Abdelkader Eslam Mohammed1,Elshaboury Nehal2,Al-Sakkaf Abobakr3

Affiliation:

1. Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt

2. Housing and Building National Research Centre, Egypt

3. 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.

Publisher

IGI Global

Subject

Decision Sciences (miscellaneous),Computational Mathematics,Computational Theory and Mathematics,Control and Optimization,Computer Science Applications,Modeling and Simulation,Statistics and Probability

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