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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
    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
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