Multi-objective optimisation model: A housing block retrofit in Seville

Abstract

Performance-driven optimization has shown its potential to integrate design and energy performance, since building shape and envelope are determinant to the energy demand. Even though new buildings should be nearly zero energy buildings by 2020, according to European Directive 2010/31 recast, they only represent a minority of the building stock. Building retrofit has demonstrated a great potential to reduce energy consumption, and at the same time, CO 2 emissions. The scope of this work is to present and test a design methodology to enable architects and decision makers to couple building shape and envelope with energy and financial performance in a multi-objective optimization method. Our multi-objective optimization model is adaptable for both conceptual design and building retrofit, and presents effective application to assist design decisions in a seamless workflow. In this case study, we have applied the model to a housing block retrofit in Seville. We have designed indexes to optimise building envelope, considering mild climates issues. Then, we have simulated heating and cooling demand of the optimal solutions and calculated Net Present Value (NPV) to obtain their financial feasibility. The results have shown the robustness and validity of the framework.

A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

“…In addition to the aforementioned features, GA extensive use in building optimisation is repeatedly attributed to: its ability to work with a population of individuals that expectedly converges to the true non-dominated Pareto front [18,77,89,117]; its flexibility and robust performance as a search method without exhausting the entire search space [18,23]; the possibility of exploring large solution domains, which is crucial in most MOO building problems, while avoiding converging to local optima as aforementioned [111,[118][119][120][121]; assuring a good tradeoff between the required computational burden and the robustness of the optimal solutions achieved [19,106,119,[122][123][124]; a solutions estimation scheme adequate to complex problems as it reduces computational time [106,[123][124][125]; obtaining suitable solutions according to the objective functions when large and sophisticated input data are given [120,121]; GA' structure, presented as the most convenient for the connection with building performance simulation tools and the management of their outputs [27]; its high efficiency in solving complex multi-modal problems when the optimisation is not smooth or when the cost function is noisy [3,111,119,126,127], integer and mixed integer optimisation problems [128] and nondifferentiable functions [129]; and being well-suited for parallel computing [4,27,42,53,100].…”

Housing and neighbourhood diagnosis for ageing in place: Multidimensional Assessment System of the Built Environment (MASBE)

“… Users’ demands are not included in the design of renovation interventions. Camporeale, Mercader-Moyano, and Czajkowski (2017) Multi-objective optimisation method for decision-making applied in Spain. Absence of social patterns for assessing certain renovation works.…”

OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling

“…Majority of the design optimization papers in the literature utilized Galapagos tool in dealing with energy [17][18][19], daylight [20,21], both energy and daylight [22,23] and structure [24][25][26].…”