Hong, G.; Seong, N. Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term. Buildings2023, 13, 2526.
Hong, G.; Seong, N. Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term. Buildings 2023, 13, 2526.
Hong, G.; Seong, N. Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term. Buildings2023, 13, 2526.
Hong, G.; Seong, N. Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term. Buildings 2023, 13, 2526.
Abstract
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. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.
Keywords
ANN; energy consumption; optimization; direct fired absorption chiller; validation
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
