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A multi-objective optimization model towards an energy efficient housing stock in new cities in Egypt
Egypt’s growing population in the past few decades has led to a significant rise in housing demands. This has directed the government to initiate a national project, with the goal of developing 50 new satellite cities in the arid desert lands by 2030, to mitigate the high population density in the major cities. The Egyptian building stock comprises around 13.5 million residential buildings, with almost 37 million residential units. More than 50% of these buildings were built in the last two decades. This has resulted in the residential sector accounting for 47% of total electrical energy consumption in Egypt, as well as 12% of the total CO2 emissions.
With the projected growth in residential buildings and the current state of energy supply and demand in Egypt, it is necessary to reduce energy consumption. This highlights the need for a more energy-efficient building stock which accounts for future climate change. The early design stage of domestic buildings is where most design decisions are made, and where there is the greatest potential to achieve high energy-efficient designs. This thesis adopts the multi-objective optimization approach, which enables a cross-comparative analysis between various design alternatives and strategies.
To achieve this goal, this research has two main objectives. Firstly, to develop a bottom-up building stock model based on representative archetypes for Egypt’s new cities that both analyze the overall stock energy consumption of buildings, and also predicts future energy demand and CO2 emissions. Secondly, to develop a multi-objective optimization framework for the developed archetypes to explore and evaluate the environmental and economic benefits of possible alternative energy-efficient design strategies. This is both for the preliminary design phase, and for possible retrofitting strategies as well. Aiming to minimize the overall energy consumption, this framework will help practiotiners and decision makers target the identification key parameters of buildings, and evaluate their relative contribution to the building energy performance.