| Title |
Development of Online Machine Learning Model for AHU Supply Air Temperature Prediction using Progressive Sampling and Normalized Mutual Information |
| Authors |
Chu, Han-Gyeong ; Shin, Han-Sol ; Ahn, Ki-Uhn ; Ra, Seon-Jung ; Park, Cheol Soo |
| DOI |
https://doi.org/10.5659/JAIK_SC.2018.34.6.63 |
| Keywords |
normalized mutual information ; online machine learning model ; progressive sampling ; Building Energy Management System ; information entropy |
| Abstract |
The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction (5.4% → 1.3%). |