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Energies 2018, 11(7), 1863; https://doi.org/10.3390/en11071863

Multi-Objective Optimisation of the Energy Performance of Lightweight Constructions Combining Evolutionary Algorithms and Life Cycle Cost

1
RISCO-Department of Civil Engineering University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
2
Polytechnic Institute of Viseu, Department of Civil Engineering, Campus Politécnico, 3504-510 Viseu, Portugal
3
CONSTRUCT-LFC, Faculty of Engineering (FEUP), University of Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Received: 19 June 2018 / Revised: 11 July 2018 / Accepted: 16 July 2018 / Published: 17 July 2018
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Abstract

This paper discusses the thermal and energy performance of a detached lightweight building. The building was monitored with hygrothermal sensors to collect data for building energy model calibration. The calibration was performed using a dynamic simulation through EnergyPlus® (EP) (Version 8.5, United States Department of Energy (DOE), Washington, DC, USA) with a hybrid evolutionary algorithm to minimise the root mean square error of the differences between the predicted and real recorded data. The results attained reveal a good agreement between predicted and real data with a goodness of fit below the limits imposed by the guidelines. Then, the evolutionary algorithm was used to meet the compliance criteria defined by the Passive House standard for different regions in Portugal’s mainland using different approaches in the overheating evaluation. The multi-objective optimisation was developed to study the interaction between annual heating demand and overheating rate objectives to assess their trade-offs, tracing the Pareto front solution for different climate regions throughout the whole of Portugal. However, the overheating issue is present, and numerous best solutions from multi-objective optimisation were determined, hindering the selection of a single best option. Hence, the life cycle cost of the Pareto solutions was determined, using the life cycle cost as the final criterion to single out the optimal solution or a combination of parameters. View Full-Text
Keywords: optimisation; evolutionary algorithms; thermal comfort; Passive House; life cycle cost optimisation; evolutionary algorithms; thermal comfort; Passive House; life cycle cost
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Oliveira, R.; Figueiredo, A.; Vicente, R.; Almeida, R.M.S.F. Multi-Objective Optimisation of the Energy Performance of Lightweight Constructions Combining Evolutionary Algorithms and Life Cycle Cost. Energies 2018, 11, 1863.

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