a b s t r a c tHigh levels of energy consumption in residential buildings and global warming are important issues. Thus the energy performance of buildings should be improved in the early stages of design. This article describes an approach for developing guidelines on sensitive and robust design parameters for the present, the 2020s, the 2050s and the 2080s. Such guidelines can help architects to design low-rise apartment buildings that require less energy for various purposes, such as heating or cooling. The article consists of a general literature review, interviews with architects, the generation of case-specific information and a mock-up presentation and a meeting with professionals. An example guideline that aims to reduce annual cooling energy loads under global warming in low-rise apartment buildings located in hot-humid climates is presented to demonstrate how the proposed approach can be applied. For this guideline, case-specific information was generated, and a global sensitivity analysis based on Monte Carlo Analysis and the Latin Hypercube Sampling technique was performed. The results show that the suggested approach is feasible and could be used to provide helpful information to architects during the design of low-rise apartment buildings with high energy performance. The most sensitive design parameters that affect annual cooling energy loads in low-rise apartment buildings were natural ventilation, window area, and the solar heat-gain coefficient (SHGC) of the glazing. The results are relevant for the present, the 2020s, the 2050s and the 2080s.

In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot-humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance (R 2 ) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size.

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Energy savings have been a major driver for improving building airtightness in the last period. Air infiltration has an important influence on energy efficiency and significantly influences the indoor air quality and pollutant distribution in residential buildings. Pressure difference lead to air permeability through the building envelope via cracks and un-controlled air leaks, which increase not only energy consumption, also cause noise from the outside and entering particles harmful to human health. Therefore, the issue of airtightness of the building envelope has been included in the standards and regulations. Building airtightness is influenced by various design parameters such as window/wall ratio, type of joinery, size of usage area, wall material and the insulation application also the quality of workmanship. In this study, the airtightness performance of 43 different residentials in Balıkesir was deter-mined by the BlowerDoor test measurement and in the context of airtightness the architectural design parameters impact was investigated. The air exchange rate (n50) values of 43 residences were obtained between 1.94 - 49.02 h-1 and compared with the existing standards. In addition, “usage area” was determined as the most effective parameter, followed by the size of the usage area, the transparency rate of the facades, the wall material type and the insulation status.