Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO2-eq vs. Discounted Payback Time

Abstract

Abstract:Building energy design is a multi-objective optimization problem where collective and private perspectives conflict each other. For instance, whereas the collectivity pursues the minimization of environmental impact, the private pursues the maximization of financial viability. Solving such trade-off design problems usually involves a big computational cost for exploring a huge solution domain including a large number of design options. To reduce that computational cost, a bi-objective simulation-based optimization algorithm, developed in a previous study, is applied in the present investigation. The algorithm is implemented for minimizing the CO 2 -eq emissions and the discounted payback time (DPB) of a single-family house in cold climate, where 13,456 design solutions including building envelope and heating system options are explored and compared to a predefined reference case. The whole building life is considered by assuming a calculation period of 30 years. The results show that the type of heating system significantly affects energy performance; notably, the ground source heat pump leads to the highest reduction in CO 2 -eq emissions, around 1300 kgCO 2 -eq/m 2 , with 17 year DPB; the oil fire boiler can provide the lowest DPB, equal to 8.5 years, with 850 kgCO 2 -eq/m 2 reduction. In addition, it is shown that using too high levels of thermal insulation is not an effective solution as it causes unacceptable levels of summertime overheating. Finally a multi-objective decision making approach is proposed in order to enable the stakeholders to choice among the optimal solutions according to the weight given to each objective, and thus to each perspective.

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].…”

“…• As stand-alone form [10,11,12,23,25,76,89,108,110,112,113,115,[117][118][119]121,[124][125][126]128,129,[131][132][133][134]141,[143][144][145][146]151,152];…”

Building Thermo-Modernisation Solution Based on the Multi-Objective Optimisation Method

“…Palonen et al [23] presented GA for the optimisation of detached building envelopes and HVAC system parameters, where the investment cost of insulation and windows was used as an objective function. In [24], the Pareto front concept was applied and bi-objective optimisation was implemented by running the modified GA in order to identify low-emission cost-effective design solutions for a single-family dwelling in the cold climate of Helsinki, Finland. Moreover, Brunelli et al [25] employed the GA for achieving sustainable design by setting five objectives: minimisation of thermal energy demand, electric energy consumption, CO 2 -eq emissions, maximisation of investment net present value (NPV) and thermal comfort.…”

Parametric multi-objective energy and cost analysis in the life cycle of nearly zero energy buildings − an exhaustive search approach

“…For that reason, multi-objective optimization analysis has become popular in recent years. In comprehensive review studies, various multi-objective approaches for building energy design were proposed, as summarized by [12][13][14][15][16]. The multi-objective approach used in these studies is usually based on the concept of Pareto frontier and genetic algorithms: The basic concept of Genetic Algorithms is designed to simulate processes in the natural system necessary for evolution [17].…”

“…Genetic algorithms were applied and further optimized within extensive frameworks for cost-optimal and nearly zero-energy building solutions by considering the minimization of energy demand/CO 2 emissions and investment or lifecycle costs as objectives [16][17][18]. The optimisation of energy and life cycle cost parameters has especially been tackled by Tuhus-Dubrow [19] who optimised several parameters of building shape and envelope features of a residential building to minimise life-cycle cost.…”