Indonesian government is accelerating the development of national housing through its stateowned company, in which all planning designs have been already established including urban design and urban revitalization. But unfortunately the detailed engineering of installation building, especially roof insulation building has not gotten much attention yet. Main contractor was paid for the project but the insulation should be an extra work to be paid due to its status as variant order. So, any kind of insulation types are pleased to be proposed. This paper proposed Material and Cost-Based methods that was studied by comparing three proposed materials from physics and economic point of view with its theory and simulation data whereas computer simulation also conducted. Evidently, insulation has an important role to be installed due to gaining solar heat reduction from solar exposure through the rooftop based on indoor thermal condition and energy of cooling utilities. Besides, those methods presented the simplified decision for company to select a beneficial material to be installed.

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

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