An Innovative Multicriteria Decision-Making Tool for Building Performance Optimization

  • A. Serbouti Hassan First University of Settat, Morocco
  • M. Rattal Hassan First University of Settat, Morocco
  • E. M. Oualim Hassan First University of Settat, Morocco
  • A. Mouhsen Hassan First University of Settat, Morocco
Volume: 11 | Issue: 1 | Pages: 6603-6608 | February 2021 | https://doi.org/10.48084/etasr.3953

Abstract

Buildings are accountable for nearly 40% of global greenhouse gas emissions. Their overall efficiency is thus a major pillar to optimize energy consumption and to mitigate engendered global warming. The current work takes part in this global dynamic. Indeed, we developed a standalone decision-aid tool based on sensitivity analysis, multiobjective optimization, and artificial neural networks to design a new generation of energy-efficient buildings. The tool aims to allow benefiting from Sobol’ sensitivity analysis samplings to instantaneously generate sensitivity indexes and perform multicriteria optimizations. This efficient process allows both understanding buildings’ complex behavior (by ranking the impact of the inputs parameters on the outputs and highlighting their interactions) and optimizing their overall performance. The main advantages of this method are the time gaining and the provision of relevant outputs to analyze the buildings’ design. The tool was successfully used to solve constrained 13-input parameters with 5-criteria on TRNSYS simulation program, considering the impact of global warming

Keywords: energy efficiency, sensitivity analysis, multiobjective optimization, polynomial regression, global warming

Downloads

Download data is not yet available.

References

2018 Global Status Report - Towards a zero-emission, efficient and resilient buildings and construction sector. International Energy Agency, 2018.

A. L. Rizor and A. Corn, "Design Criteria and Solutions to Common Issues in Building Envelope Design," in Building Science and the Physics of Building Enclosure Performance, D. J. Lemieux and J. Keegan, Eds. West Conshohocken, PA, USA: ASTM International, 2020, pp. 195-210. DOI: https://doi.org/10.1520/STP161720180064 DOI: https://doi.org/10.1520/STP161720180064

J. Vivian, U. Chiodarelli, G. Emmi, and A. Zarrella, "A sensitivity analysis on the heating and cooling energy flexibility of residential buildings," Sustainable Cities and Society, vol. 52, Jan. 2020, Art. no. 101815. DOI: https://doi.org/10.1016/j.scs.2019.101815 DOI: https://doi.org/10.1016/j.scs.2019.101815

M. Rohani, G. Shafabakhsh, A. Haddad, and E. Asnaashari, "Sensitivity Analysis of Workspace Conflicts According to Changing Geometric Conditions," Engineering, Technology & Applied Science Research, vol. 7, no. 1, pp. 1429-1435, Feb. 2017. DOI: https://doi.org/10.48084/etasr.1012 DOI: https://doi.org/10.48084/etasr.1012

Z. Yong, Y. Li-juan, Z. Qian, and S. Xiao-yan, "Multi-objective optimization of building energy performance using a particle swarm optimizer with less control parameters," Journal of Building Engineering, vol. 32, Nov. 2020, Art. no. 101505. DOI: https://doi.org/10.1016/j.jobe.2020.101505 DOI: https://doi.org/10.1016/j.jobe.2020.101505

H. R. Mafakheri, A. H. Hejazi, and M. Dashti, "Design of the Administrative Building of Kuhsar City under a Sustainable Architecture Concept Approach," Engineering, Technology & Applied Science Research, vol. 6, no. 4, pp. 1080-1083, Aug. 2016. DOI: https://doi.org/10.48084/etasr.723 DOI: https://doi.org/10.48084/etasr.723

D. Mazzeo, N. Matera, C. Cornaro, G. Oliveti, P. Romagnoni, and L. De Santoli, "EnergyPlus, IDA ICE and TRNSYS predictive simulation accuracy for building thermal behaviour evaluation by using an experimental campaign in solar test boxes with and without a PCM module," Energy and Buildings, vol. 212, Apr. 2020, Art. no. 109812. DOI: https://doi.org/10.1016/j.enbuild.2020.109812 DOI: https://doi.org/10.1016/j.enbuild.2020.109812

M. Palonen, M. Hamdy, and A. Hasan, "MOBO a new software for multi-objective building performance optimization," in Proceedings of the 13th Internationcal Conference of the IBPSA, 2013.

A. Serbouti, M. Rattal, A. Boulal, M. Harmouchi, and A. Mouhsen, "Application of sensitivity analysis and genopt to optimize the energy performance of a building in Morocco," International Journal of Engineering & Technology, vol. 7, no. 4, pp. 2068-2074, Sep. 2018. DOI: https://doi.org/10.14419/ijet.v7i4.13280 DOI: https://doi.org/10.14419/ijet.v7i4.13280

A. Serbouti, M. Rattal, A. Boulal, E. M. Oualim, and A. Mouhsen, "Multi-Objective Optimization of a Family House Performance and Forecast of its Energy Needs by 2100," International Journal of Engineering & Technology, vol. 7, no. 4.32, pp. 7-10, Dec. 2018.

J. Herman and W. Usher, "SALib: An open-source Python library for Sensitivity Analysis," Journal of Open Source Software, vol. 2, no. 9, Jan. 2017, Art. no. 97. DOI: https://doi.org/10.21105/joss.00097 DOI: https://doi.org/10.21105/joss.00097

D. Brockhoff and T. Tušar, "Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed," in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, Jul. 2019, pp. 1905-1911 DOI: https://doi.org/10.1145/3319619.3326896 DOI: https://doi.org/10.1145/3319619.3326896

F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, Nov. 2011.

Core Writing Team, L. Mayer, and R. K. Pachauri, Eds., Climate change 2014: synthesis report. Geneva, Switzerland: IPCC, 2015.

M. C. Hänsel et al., "Climate economics support for the UN climate targets," Nature Climate Change, vol. 10, no. 8, pp. 781-789, Aug. 2020. DOI: https://doi.org/10.1038/s41558-020-0833-x DOI: https://doi.org/10.1038/s41558-020-0833-x

Règlement thermique de construction au Maroc (RTCM). ADEREE, 2014.

T. Kusuda, O. Piet, and J. W. Bean, "Annual variation of temperature field and heat transfer under heated ground surfaces (slab-on-grade floor heat loss calculation)," Building science series, vol. 1983, no. 156. DOI: https://doi.org/10.6028/NBS.BSS.156 DOI: https://doi.org/10.6028/NBS.BSS.156

G. Florides and S. Kalogirou, "Ground heat exchangers-A review of systems, models and applications," Renewable Energy, vol. 32, no. 15, pp. 2461-2478, Dec. 2007. DOI: https://doi.org/10.1016/j.renene.2006.12.014 DOI: https://doi.org/10.1016/j.renene.2006.12.014

KBOB : Données des écobilans dans la construction 2009/1:2014. IPB, KBOB, 2014.

B. Iooss, "Revue sur l'analyse de sensibilité globale de modèles numériques," Journal de la Societe Française de Statistique, vol. 152, no. 1, pp. 1-23, 2011.

I. M. Sobol′, "Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates," Mathematics and Computers in Simulation, vol. 55, no. 1, pp. 271-280, Feb. 2001. DOI: https://doi.org/10.1016/S0378-4754(00)00270-6 DOI: https://doi.org/10.1016/S0378-4754(00)00270-6

A. Gelman and G. Imbens, "Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs," Journal of Business & Economic Statistics, vol. 37, no. 3, pp. 447-456, Jul. 2019. DOI: https://doi.org/10.1080/07350015.2017.1366909 DOI: https://doi.org/10.1080/07350015.2017.1366909

F. Ascione et al., "A real industrial building: Modeling, calibration and Pareto optimization of energy retrofit," Journal of Building Engineering, vol. 29, May 2020, Art. no. 101186. DOI: https://doi.org/10.1016/j.jobe.2020.101186 DOI: https://doi.org/10.1016/j.jobe.2020.101186

Metrics

Abstract Views: 173
PDF Downloads: 118

Metrics Information
Bookmark and Share