Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm
- Authors
- Kim, Jee-Heon; Seong, Nam-Chul; Choi, Wonchang
- Issue Date
- 1-Aug-2019
- Publisher
- MDPI
- Keywords
- chiller energy consumption; artificial neural network (ANN); HVAC
- Citation
- ENERGIES, v.12, no.15
- Journal Title
- ENERGIES
- Volume
- 12
- Number
- 15
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/1112
- DOI
- 10.3390/en12152860
- ISSN
- 1996-1073
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 공과대학 > 건축학부 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.