Evaluating Performance of Simulated Annealing And Genetic Algorithm Based Approach in Building Envelope Optimisation

Varma, Piyush; Bhattacharjee, Bishwajit

doi:10.26868/25222708.2015.2514

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In this regards, some authors [10][11][12][13] provided a state-ofthe-art overview on the commonly used optimization algorithms to find the optimal scenario, other researchers compared different available algorithms. [14][15][16][17] In addition to the optimization algorithms, method, and optimization tool are also crucial to solve MOO problems. Two main optimization methods were bibliographically investigated by Costa-Carrapiço et al 18 the weighted sum approach and the Pareto-based optimization concept; the authors concluded that the percentage use of the second method in their reviewed studies represented about 80%.…”

Section: Introductionmentioning

confidence: 99%

Energy-saving potential assessment of a classroom building envelope through sensitivity analysis and multi-objective optimization under different climate types

Benaddi¹,

Boukhattem²,

Nouh

et al. 2023

Building Services Engineering Research and Technology

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The present work aims to optimize the thermal behavior of a building envelope by combining sensitivity analysis (SA) and multi-objective optimization (MOO). An existing classroom located in Marrakech city was considered a case study building. The building model was analyzed under six Moroccan climate zones. The SA was applied on 16 design variables and performed using the Morris method implemented in the tool Simlab to rank each design variable based on its influence on the objective function (overall energy demand). The SA results showed that the solar absorptance of the internal roof, wall, and ground floor and the ground hollow core slab thickness impacted less the overall energy demand. Therefore, the only remaining variables showing the most relevant effect will be optimized afterward. The optimization phase was conducted by coupling the generic optimization tool GenOpt with TRNSYS. The optimum solution was selected based on the Pareto front approach. The obtained results assessed the effectiveness of the adopted methodological approach in significant minimization of the required thermal loads. Furthermore, the values of each optimum design variables set differ from one climate zone to another; leading to energy demand reduction varying from 30 to 42%, in comparison with the original design building.

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