| Title |
An Analysis of the Prediction Accuracy of HVAC Fan Energy Consumption According to Artificial Neural Network Variables |
| Authors |
Kim, Jee-Heon ; Seong, Nam-Chul ; Choi, Won-Chang ; Choi, Ki-Bong |
| DOI |
https://doi.org/10.5659/JAIK_SC.2018.34.11.73 |
| Keywords |
Building Energy ; HVAC ; Artificial Neural Networks |
| Abstract |
In this study, for the prediction of energy consumption in the ventilator, one of the components of the air conditioning system, the predicted results were analyzed and accurate by the change in the number of neurons and inputs. The input variables of the prediction model for the energy volume of the fan were the supply air flow rate, the exhaust air flow rate, and the output value was the energy consumption of the fan. A predictive model has been developed to study with the Levenbarg-Marquardt algorithm through 8760 sets of one-minute resolution. Comparison of actual energy use and forecast results showed a margin of error of less than 1% in all cases and utilization time of less than 3% with very high predictability. MBE was distributed with a learning period of 1.7% to 2.95% and a service period of 2.26% to 4.48% respectively, and the distribution rate of ±10% indicated by ASHRAE Guidelines 14 was high.8 |