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is that they do not perform the sensitivity analysis or the robustness
test on the model. According to [11] every model has a high proba-
bility of having uncertainty with regard to some of its parameters.
This issue can be addressed by performing the sensitivity analysis
or the robustness test. In the study [12] a sensitivity analysis is used
before the decision making to validate the robustness of the design
decision related to the energy consumption and comfort. The study
in [13] makes use of sensitivity analysis to predict the night cooling
performance of internal convective heat transfer modelling and the
result reveals that some choices of the convectional algorithm may
affect the energy and predictions related to the thermal comfort.
The study in [14] inspects the robustness of the methodology used
to estimate the hourly energy consumption of a given building that
considers discrepancies of the parameters within a building. The
results show that the methodology can eliminate the errors caused
by discrepancies. The research in this paper addresses these short-
comings of the previous researches by constructing a model that
will maximise the energy savings and minimise the payback period
of the investment, and there will be trade-off between the two if
necessary. The contribution of this paper is the addition of the pay-
back period of the investment as an objective, something that has
never been considered by previous studies. A sensitivity analysis is
performed to illustrate the robustness of the model. The model is
constrained by budget, targeted energy savings and acceptable pay-
back periods. This model also considers the time value of money by
making use of the net present value (NPV). The research conducted
by [8,9] present a model that is applicable to the design phase of the
building, while the research under this study will present a model
that can be used during the operation stage of the building. The
model in this paper is applicable to many similar energy retrofit
and renovation projects.
Because of the complexity of the multi-objective optimisation