  Within the environmental context, numerical modeling is a promising approach
to assessing the energy efficiency of buildings. Resilient buildings need to be
designed, and capable of adapting to future extreme heat. Simulations are
required assuming a one-dimensional heat transfer problem through walls and a
simulation horizon of several years (nearly 30). The computational cost
associated with such modeling is quite significant and model reduction methods
are worth investigating. The objective is to propose a reliable reduced-order
model for such long-term simulations. For this, an alternative model reduction
approach is investigated, assuming a known Proper Orthogonal Decomposition
reduced basis for time, and not for space as usual. The model enables computing
parametric solutions using basis interpolation on the tangent space of the
\textsc{Grassmann} manifold. Three study cases are considered to verify the
efficiency of the \revision{reduced-order} model. Results highlight that the
model has a satisfying accuracy of $10^{\,-3}\,$ compared to reference
solutions. The last case study focuses on the wall energy efficiency design
under climate change according to a \revision{four-dimensional} parameter
space. The latter is composed of the load material emissivity, heat capacity,
thermal conductivity, and thickness insulation layer. Simulations are carried
over $30$ years considering climate change. The solution minimizing the wall
work rate is determined with a computational ratio of $0.1\%$ compared to
standard approaches.
