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A multiple objective optimisation model is formulated to help decision makers to make an optimal decision when investing in energy-efficient building retrofitting. The objectives are to maximise the energy savings and minimise the payback period for a given fixed initial investment. The model is formulated as a multi-objective optimisation problem with the net present value (NPV), initial investment, energy target and payback period as constraints and it is solved using genetic algorithms (GAs). The optimal decision is reached by choosing the most optimal actions during energy retrofit in buildings. The model is applied to a case study of a building with 25 facilities that can be retrofitted that illustrates the potential of high energy savings and short payback periods. The sensitivity analysis is also performed by analysing the influence of the auditing error of the facilities, wrongly specified energy savings, the initial investment,changes in interest rate and the changes of electricity prices on the payback period, the maximum energy saved and NPV of the investment. The outcome of this analysis proves that the model is robust.1. Introduction 20220
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The current energy shortage around the world is the reason that
energy efficiency is a subject of interest today. The most viable
option to counteract this problem is by reducing the current energy
consumption. With buildings consuming around 40% of the world’s
total energy [1], it would be beneficial to invest in building energy
efficiency retrofit projects. In order to improve the energy effi-
ciency of buildings, inefficient facilities are often replaced by highly
advanced energy efficient ones. A whole range of facilities can be
retrofitted if there is unlimited funding, although usually this is not
the case. Nevertheless the following are some of the retrofit actions
that can be taken [2–4]:
• Building improvements – insulating the roof, replacing the sin-gle glazing windows with double glazing windows and installing
solar shading.
• HVAC system improvements – installing energy efficient systems with advanced controls.
• Energy efficient lighting – replacing incandescent lighting by compact fluorescent lighting (CFL) or LED lighting.
• Replacing inefficient equipment – replacing cathode ray tube
(CRT) computer monitors with liquid crystal displays (LCD).
• Electromechanical improvements – installing power factor cor-
recting capacitors to improve the power factor.
The main problem is that most investors are reluctant to invest
in energy saving projects such as retrofit projects. This is because
such projects are often not able to compete with other investments
within the institutions or companies due to unclear benefits. But
this is not the case if an investment in energy-efficiency projects
is made with the help of decision making tools that can identify
large monetary savings. Furthermore, this makes energy efficiency
projects competitive with other projects. A decision can be made
using the following two approaches [5–9]:
• In the first approach, an energy expert carries out an energy