| Summary: | With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30% of total energy has been consumed by buildings, and much attention has been paid to reducing building energy consumption. There are many ways to reduce building energy consumption. One of the most relevant methods is machine learning. While machine learning methods provide accurate energy consumption predictions, they require huge datasets. The present study developed an artificial neural network (ANN) model for building energy consumption predictions with small datasets. As mechanical systems are the most energy-consuming components in the building, the present study used the energy consumption data of a direct-fired absorption chiller for the short term. For the optimization, the prediction results were investigated by varying the number of inputs, neurons, and training data sizes. After optimizing the ANN model, it was validated with the actual data collected through a building automation system. In sum, the outcome of the present study can be used to predict the energy consumption of the chiller as well as improve the efficiency of energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets, which can improve the efficiency of building energy management.
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