Building Performance Optimization of Net Zero-Energy Buildings - Modeling, Design, and Optimization of Net-Zero Energy Buildings - page 260

because it involves complex dynamic interactions. Optimization
algorithms can help in finding the optimal and near-optimal solutions
regarding the design and sizing of passive and active energy systems
and finding the balance between demand and production.
- Achieving cost-effective Net ZEBs by analyzing and synthesizing
multi-physics systems that may include passive and active facades,
lighting controls, natural ventilation, HVAC, and storage of heat in the
building structure combining advanced technologies, such as
micro-CHP, BIPV, BIPV/T, solar thermal collectors, and microwind
turbines. The complexity of such systems poses a serious challenge to
designers. The use of BPO is an opportunity to inform designers of
optimal and cost-effective design decisions during building design and
operation.
- Allowing optimal systems scheduling through Model Predictive
Control (MPC) taking into account the dynamics of Net ZEB systems
and anticipated future energy load. When solving the optimal control
problem using the MPC algorithm, it determines near-optimal control
settings during design and operation are determined and the
load-matching problem is addressed.
5.3 Application of Optimization: Cost-Optimal and
Nearly Zero-Energy Building
5.3.1 Introduction
According to the recast of the European Energy Performance of Buildings
Directive (EPBD-r) (European Parliament and Council, 2010), the
minimum energy performance requirements of buildings should be set with
the aim of achieving cost-optimal levels for buildings, building units, and
building elements (Constantinescu, 2010). Higher-energy performance
levels, like net-zero energy, should also be economically feasible. The EPBD
indicates that all new buildings should be “nearly zero-energy buildings”
(Nearly ZEB) by the end of 2020, and two years prior to that for public
buildings. According to the Recital 15 of the EPBD-r