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Nowadays a large portion of energy consumption is depends on Residential Building. For these reasons Mechanical ventilation, colling, heating such things also be improved. But Occupants take action to control their indoor environments to adjust their individual flavour. Window Opening &amp; closing If this result is universally applicable most preferred behaviour to adjust their indoor environment. However these behaviour causes an increase in energy consumption in the building. Therefore, if the window opening and closing behavior can be predicted, it will lead to an increase the comfort in the building and a decrease in the energy consumption. Previous studies have confirmed that window opening and closing behavior is significantly affected by ambient temperature. This also indicates that the season also affects window opening. It can be seen that the machine learning algorithm has better prediction than the traditional regression model. The purpose of this study is to investigate the effects of building information (location, number of floors, area, completion year) and human characteristic for indoor environmental control(heating setting temperature), not on the environmental parameters(temperature, humidity, fine dust, etc.) applied to previous machine learning models in the prediction of window opening and closing behavior. If this result is normally applicable, it will make more realistic predictions possible than when predicting with existing environmental parameters alone. This will give a plausibility to the results of simulations on actual buildings.

Effects of building parameters on occupant’s window opening behaviour