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Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined. c 2015 KIEAE Journal      Optimal Controls Predictive Model Input Variables Thermal Environment Accommodations A C C E P TA N C E IN F O Received June 20, 2015 Final revision received July 8, 2015 Accepted July 10, 2015              106 KIEAE Journal, Vol. 15, No. 4, Aug. 2015 Moon and Kim         < 1>     .     .      (Physical conditions)            .               .                   .             ,        .     (setback temperature)           (setpoint temperature)    (TIMESPT)        .   < 1>                   . ,     ,                 . ,              . Fig. 1. Five steps for controlling indoor thermal conditions

Input Variable Decision of the Predictive Model for the Optimal Starting Moment of the Cooling System in Accommodations