A technique to improve the design of near-zero energy buildings
Résumé
In the design context of Near-Zero Energy Buildings (nZEBs) and smart cities, robust and versatile optimization methods are needed to be coupled to simulation tools. In this way, the paper presents optimization algorithms coupled to a software with a capability to precisely simulate solar radiation availability by using a graphical pixel counting technique and by integrating with external models via Functional Mockup Interface (FMI). The optimization is based on mono- and multi-objective algorithms to solve a case study problem with two objectives to optimize: i) the cooling energy demand and ii) the payback period. Then, an economic viability model is presented, considering construction aspects such as insulation thickness, energy consumption and the number of installed solar panels. The algorithms are applied and compared for a building based on the BESTEST 910 case, considering tropical weather of Rio de Janeiro, Brazil. Both algorithms succeed, but with different characteristics related to computer run time and accuracy.
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