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

Rui Oliveira, António Figueiredo, Romeu Vicente and Ricardo M. S. F. Almeida
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Rui Oliveira: RISCO-Department of Civil Engineering University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
António Figueiredo: RISCO-Department of Civil Engineering University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Romeu Vicente: RISCO-Department of Civil Engineering University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Ricardo M. S. F. Almeida: Polytechnic Institute of Viseu, Department of Civil Engineering, Campus Politécnico, 3504-510 Viseu, Portugal

Energies, 2018, vol. 11, issue 7, 1-23

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.

Keywords: optimisation; evolutionary algorithms; thermal comfort; Passive House; life cycle cost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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