Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms

Ferdinando Salata (Corresponding author), Virgilio Ciancio, Jacopo Dell'Olmo, Iacopo Golasi, Olga Palusci, Massimo Coppi

Research output: Contribution to journalArticleAcademicpeer-review

63 Citations (Scopus)


The energy requalification of existing buildings entails the fulfillment of different, often conflicting, criteria, such as the reduction of the specific annual energy demand, the containment of the construction costs, the decrease in the annual energy operating cost and the reduction of climate-change gas emissions. Therefore, optimization methods based on the application of computational algorithms are essential to determine solutions that meet multi-objective criteria and so highly optimized to be on the Pareto frontier. In this work, a procedure for the optimization of existing buildings using genetic algorithms is presented. Building energy simulations conducted in the dynamic regime using EnergyPlus are coupled with an Active Archive Non-dominated Sorting Genetic Algorithm (aNSGA-II type). Using a residential building as a benchmark, this procedure is employed to evaluate the best retrofitting interventions for 19 European cities with different climates. The criteria taken into account in the optimization procedure are: the reduction in the annual specific energy demand, the decrease in the construction and installation costs, the reduction in the annual energy operating costs and the reduction in the greenhouse gas emissions. The results show the most advantageous energy retrofitting interventions fulfilling the criteria for the different geographical sites.
Original languageEnglish
Article number114289
Number of pages18
JournalApplied Energy
Publication statusPublished - 15 Feb 2020


This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. In particular, Ferdinando Salata gratefully acknowledges Sapienza University of Rome for the support deriving from his personal grant “Progetti di Ricerca - Progetti Medi 2018”.

FundersFunder number
University of Rome La Sapienza


    • NZEB
    • Genetic algorithm
    • multi-objective optimization
    • Energy efficiency
    • Climate conditions
    • EnergyPlus
    • Multi-objective optimization
    • nZEB


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