| Title |
Short-Term Load Prediction Using Artificial Neural Network Models |
| Authors |
Byung Ki Jeon ; Eui-Jong Kim |
| DOI |
https://doi.org/10.6110/KJACR.2017.29.10.497 |
| Keywords |
인공신경망 ; 건물 부하 ; 기상 데이터 Artificial neural network ; Building load ; Weather data |
| Abstract |
In recent years, studies on the prediction of building load using Artificial Neural Network (ANN) models have been actively conducted in the field of building energy In general, building loads predicted by ANN models show a sharp deviation unless large data sets are used for learning. On the other hands, some of the input data are hard to be acquired by common measuring devices. In this work, we estimate daily building loads with a limited number of input data and fewer pastdatasets (3 to 10 days). The proposed model with fewer input data gave satisfactory results as regards to the ASHRAE Guide Line showing 21% in CVRMSE and -3.23% in MBE. However, the level of accuracy cannot be enhanced since data used for learning are insufficient and the typical ANN models cannot account for thermal capacity effects of the building. An attempt proposed in this work is that learning procersses are sequenced frequrently and past data are accumulated for performance improvement. As a result, the model met the guidelines provided by ASHRAE, DOE, and IPMVP with by 17%, -1.4% in CVRMSE and MBE, respectively. |