The energy performance in two administrative office buildings in Egypt is evaluated. One building is a traditional classic building while in the other building the designer consider energy efficiency and solar energy generated power contribution to the power required. The office buildings in this study has been designed with the compliance of the building regulation during their contraction time. The evaluation is done by ENERGY STAR. The main conclusions were that ENERGY STAR can be used to evaluate energy performance of office buildings in Egypt. Egyptian office buildings can compete the USA office buildings in applying energy efficiency strategies. In addition, energy efficient lighting and HVAC systems used in the new building save 19% and 64% of the energy used respectively and will save about 46% from the source energy used. In addition the GHG emissions / m2 in the new building is half that of the existing one.

Abstract: Energy use in buildings is closely linked to their design (form, orientation and building materials), operational and space utilization characteristics and the behaviour of their occupants. Due to demand for sustainability, more passive buildings will be built in Egypt. This study evaluates the energy performance in a newly designed office building in Egypt. In this building, energy efficiency aspects were considered during the design. The evaluation is done using two different tools - Portfolio Manager (PM) and Target Finder (TF) - related to ENERGY STAR (ES) using the Energy Use Intensities (EUI). The only difference is that PM needs at least 12 consecutive months of metered utility bills to perform the evaluation calculations, while TF needs an estimated annual energy use for each type of energy consumed in the building. Energy consumption was determined by two ways: simulation and calculation. DesignBuilder (DB) has been used in the current study to simulate the building energy use for a whole year considering building design, building materials, optimization of thermal comfort, lighting, weather data and activity in the case study building. In addition, calculations for annual energy use were done using the data sheets with each energy consuming system utilized in the building based on operating hours of each. The ES scores for both simulation and calculation results were obtained. The main conclusions were that ES can be used to evaluate energy performance of office buildings in Egypt. Moreover, the results showed that the Egyptian office buildings can compete with the USA office buildings in applying energy efficiency strategies. Results of the optimizations showed significant reduction in terms of energy consumption. Finally, the simulation results showed the relative effect of the applied design, shading, orientation and the energy efficient lighting and HVAC systems on the annual energy consumption and ES score.

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Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time

Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time

Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time.