Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Jee-Heon Kim, Nam-Chul Seong and Wonchang Choi
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Jee-Heon Kim: Eco-System Research Center, Gachon University, Seongnam 13120, Korea
Nam-Chul Seong: Eco-System Research Center, Gachon University, Seongnam 13120, Korea
Wonchang Choi: Department of Architectural Engineering, Gachon University, Seongnam 13120, Korea

Energies, 2019, vol. 12, issue 15, 1-13

Abstract: This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

Keywords: chiller energy consumption; artificial neural network (ANN); HVAC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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