Abstract
The challenge in Zero energy building (ZEB) design is to find the best combination of design strategies that would face the energy performance problems of a particular building. This paper outlines the methodology and the cost-effectiveness potential for optimizing the design of net-zero energy building (NZEB) in a cold climate region in Lebanon; Cedars. Specifically, the non-sorting genetic algorithm (NSGA-II) is chosen in order to minimize thermal, electrical demands and life cycle cost (LCC) while reaching the net zero energy balance; and thus getting the Pareto-front. A ranking decision making technique (ELECTRE III) is applied to the Pareto-front so as to obtain one optimal solution. A wide range of energy efficiency measures are investigated, besides solar energy systems are employed to produce required electricity and hot water for domestic purposes. The results clearly indicate that, for designing a residential NZEB in cold climate, it is essential to minimize the space thermal load through a building envelope with high thermal performance. Envelop high level of insulation is an essential step to decrease the high heating demand. Building thermal loads are decreased by 33.19%. Moreover the LCC is decreased by 31.09%.
Highlights
Buildings’ energy demand is estimated to keep increasing in the decades
It is noticed that SDHW electric consumption is reduced by 17.91%, thermal energy consumption is decreased by33.19% through optimal passive designs compared to the base case, and life cycle cost (LCC) is reduced by 31.09%
The results clearly indicate that there is a significant potential to improve the energy performance of residential buildings in cold climate of Cedars by using proven passive strategies
Summary
Buildings’ energy demand is estimated to keep increasing in the decades. By the end of 2014, buildings (residential, commercial and public) represented about 49% of the world’s electricity consumption, where the residential sector accounts for 27% of the total electrical use [1]. Building optimization is an effective technique to evaluate design choices and to get the perfect solution for a specific intention expressed as objective functions under several constraints [4]. Multi-objective optimization (MOO) results are sets of non-dominated solutions called Pareto optimal solutions represented as a Pareto frontier [5]. Once the Pareto frontier is obtained, here comes the importance of the multicriterion decision-making (MCDM) process in order to select the final optimal solution among all available possibilities. Cooling loads are covered by air source heat pumps, characterized by a coefficient of performance (COP) equal to 2.9. During unoccupied hours, both cooling and heating systems are turned off. The building is considered as tight, so the infiltration rate is equal to 0.38 ACH [8]
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