• The Journal of Bigdata
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• v.9 no.1
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• pp.153-168
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• 2024

In summer, as chillers are considered the main energy consumer of building, the efficient chiller operation is considered important. However, it is difficult to operate chillers to meet the cooling demand of the building as the demand fluctuates with various factors like the internal, external environment and behavior of the occupants and as chiller's constraint cause the current operation constrains operation in future. To address these problems, this study proposes a multi-chiller operation model based on deep reinforcement learning considering the minimum up-time of the chiller. The proposed model learns the value of the chiller operations according to the state composed of metrological and cooling system information and determines operation that minimizes the difference between the supply load and the cooling demand among feasible operations. The practical applicability was improved by applying the training algorithm considering the minimum up-time constraint and Experiments results using the actual data from a Korean university confirmed that the proposed model complies with the chiller constraints and outperforms the existing chiller operation logic of the university in terms of differences from the building cooling demand.