2 results listed
With the development of technology from past to present, the types, properties and
product range of the materials used in the buildings are quite developed. Therefore, the effects of
climate, environmental conditions and energy consumption cannot be ignored for selecting these
materials used in the buildings. Usage of materials with the same characteristics for buildings to
be built on different climate may lead to adverse effects about energy-saving and green gasses.
Furthermore, the use of the same materials may not be a proper approach even in buildings with a
different purpose. In this study, forecasting of energy consumption and CO2 emission is analyzed
by utilizing artificial neural network structure according to different climate criteria and material
characteristics for public buildings built in recent years. The Effect levels to energy consumption
and CO2 emission of the building materials and the climate criteria are determined for buildings
serving the same using purpose in different climate characteristics. For the study, different pilot
regions where the public buildings are located are chosen according to climatic characteristics and
five different building materials used in these public buildings are taken into account. When the
results compare according to CO2 emission and energy consumption, it was observed that the
conditions which obtain most efficient results are different
International Data Science & Engineering Symposium
IDSES
Salih HİMMETOĞLU
Yılmaz DELİCE
Emel KIZILKAYA AYDOĞAN
In today's urban life, most of the people's time is spent at home or work office. For this
reason, design and selecting of building materials that control heating (H), ventilation (V) and air
conditioning (AC) are essential. The building materials called, in short HVAC, must
simultaneously provide high comfort, low cost and high energy productivity. Furthermore, HVAC
must be appropriately designed to prevent adverse effects on the environment and climate. In this
study, nine different HVAC systems were examined according to nine different criteria over cost,
pollution, comfort and energy which are considered as four main factors in the selection of HVAC
systems. Since some of the criteria for HVAC systems are described as linguistic, it is not possible
to evaluate the systems with traditional methods using crisp values. Therefore, we propose
generalized intuitionistic fuzzy (IF) - rough set models which are a new and flexible method. IFneighborhoods
are formed by using IF-implicator and IF-t norms, and upper and lower
approximations in rough set theory are calculated according to the neighborhoods. Covering-based
generalized IF rough set models are generated by using the approximations and IF-TOPSIS
method. According to the obtained results, we can see that the proposed method is an appropriate
decision-making method which considers the uncertainties in the linguistic expressions for
selecting the most suitable HVAC system.
International Data Science & Engineering Symposium
IDSES
Salih HİMMETOĞLU
Emel KIZILKAYA AYDOĞAN
Yılmaz DELİCE