الفهرس 
The Building Energy Simulation (BES)Only 14 pages are availabe for public view
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Abstract
The use of Building Energy Simulation Tools (BESTs) is becoming widely common among building engineers and architects as the need for sustainable buildings keeps growing. The amount of challenges within the energy optimization process is also increasing as (BESTs) are developing to become more detailed. Major challenges such as uncertainty and sensitivity within the energy simulation process and the multivariate property within the energy optimization process are being widely discussed nowadays.
The power of today’s computational methods within the field of Artificial Intelligence (AI), and today’s proven application of AI branches, such as: expert systems, machine learning, and deep learning; this power, in fusion with the well-established science of Decision Support Systems (DSS), forms Intelligent Decision Support Systems (IDSS); which is expected by the researcher to provide BESTs with the needed intelligence and expertise to be able to solve the existing building’s energy simulation challenges, and smoothing the BES process as well.
The multi-disciplinary parametric property of the optimization process, and the occurring variation in constraints along the design and construction phases do also require an energy simulation tool that is capable of co-oping like an expert with this change in different parameters all along the building process timeline; this tool must exist as an aiding tool to the building engineer from the moment the energy model is built, all through the construction phase, and until building operation, this tool must be also capable of producing justifications, and provide certainty and sensitivity assistance to the engineer, for providing more rigid energy related decisions during the model‘s lifecycle.
Therefore, the thesis aims to study the possible integration between the Buildings’ Energy Simulation Tools (BESTs) with the Intelligent Decision Support Systems (IDSS), as an attempt to reach a theoretical framework of a buildable IDSS-aided BEST; BEST-IDSS.