Hanon Systems

Makinarocks Co., Ltd.

Joongjae Kim

Jeonghoon Lee

Sanghyeok Choi

Minseop Kim

Ikchan Ju

Jongwon Park

Hongje Park

Taeho Lee

Minjoo Lee

Kyeong Min Woo

Sungho Joo

Wonshick Ko

Je Yeol Lee

Disclosed is a method for control simulation based on artificial intelligence according to an exemplary embodiment of the present disclosure. Specifically, according to the present disclosure, a computing device obtains a first state information, a second state information, and a control information, and generates first output information based on the first state information, the second state information, and the control information by using an artificial neural network model including a sequential neural network. In this case, the first output information includes one or more output variables, and at least some of the one or more output variables correspond to variables included in the second state information, and the first output information is generated based on applying an attention mechanism to each of the one or more output variables.

Control simulation method based on artificial intelligence

Disclosed is a method for predicting areas of environmental information needed to be collected, which is performed by one or more processors of a computing device. The method may include: outputting one or more episodes based on environmental information; measuring uncertainty for each of the one or more episodes; and predicting an area of the environmental information needed to be collected based on the measured uncertainty.

Method for predicting the areas of information needed to be collected

Disclosed is a method for controlling an air conditioning device, which is performed by at least one computing device, which includes: determining a control action for the air conditioning device at a first time point by using a reinforcement learning agent; determining a reward for the control action at the first time point based on a reward delay time by using the reinforcement learning agent; and performing reinforcement learning related to the control of the air conditioning device based on the determined reward, in which a time point when the reward delay time elapses from the first time point corresponds to a second time point, and the reward for the control action at the first time point is calculated while excluding situations after the first time point and before the second time point.

Method for controlling air conditioning device based on delayed reward

Disclosed is a method for controlling an energy management system that is performed by a computing device including at least one processor. The method may include acquiring a target temperature of one or more target points; and controlling one or more control variables using a reinforcement learning control model trained for a first condition regarding a state before a current temperature of the target points converges to the target temperature.

Method and apparatus for control energy management system based on reinforcement learning

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Daejeon, South Korea

Joongjae Kim