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.
Salata, F., Ciancio, V., Dell'Olmo, J., Golasi, I., Palusci, O., & Coppi, M. (2020). Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms. Applied Energy, 260, . https://doi.org/10.1016/j.apenergy.2019.114289