Building Performance Optimization of Net Zero-Energy Buildings - Modeling, Design, and Optimization of Net-Zero Energy Buildings - page 262

Fig. 5.8 Cost-optimal curve
Here, we summarize a multi-stage optimization method for cost-optimal
and Nearly ZEB solutions in line with the EPBD-recast 2010. The method
(Hamdy, Hasan, and Siren, 2013) provides efficient, transparent, and
time-saving explorations:
- Efficient exploration is performed by combining a two-step
optimization approach (PR_GA) (Hamdy et al. , 2009, Hamdy, Hasan,
and Siren, 2009) and a detailed building performance simulation
program (IDA-ICE 4.0). In the first optimization phase, a
single-objective deterministic algorithm is used to minimize the
two-objective functions (PEC and dLCC) one by one, sequentially, then
to minimize the first objective considering maximum value of the
second as a constraint. From the evaluations' history of the first
optimization step, optimal solutions are found by sorting code and fed
as a seed (a good initial population sample) to the second optimization
step, continuing the optimization process by multi-objective genetic
algorithm, which is a variant of the Non-dominated Sorting Genetic
Algorithm-II (NSGA-II) by Deb et al. (2002). This two-step
optimization approach improves the quality and the repeatability of the
optimization results,