| Title |
LSTM-based Prediction of Building Power Consumption with PCA Based Data Dimension Change |
| Authors |
백정엽(Baek, Jeong-Yeop) ; 민세웅(Min, Sae-Woong) ; 백대화(Baek, Dae-Hwa) ; 장성주(Chang, Seong-Ju) |
| DOI |
https://doi.org/10.5659/JAIK.2021.37.9.137 |
| Keywords |
Machine Learning; PCA; Building Energy Demand Prediction; LSTM; Smart Grid |
| Abstract |
Building energy demand currently accounts for 30% of the total energy consumption, which has a great influence on the planning and
operation of the energy market managed by energy suppliers. Furthermore, its importance has increased significantly with the advent of smart
grid. Variables affecting building energy consumption include identified various environmental conditions that cast sophisticated effect on the
energy performance of the buildings. However, due to a large number of potentially associated environmental variables, it is needed to extract
embedded features so as to improve building energy prediction capability through adopting Principle Component Analysis which could reduce
input data dimension. The primary objective of this study is to propose a high-precision building energy demand prediction model by
reducing the dimensionality through PCA. Machine learning is implemented by using LSTM model, and prediction accuracy and performance
are verified through , RMSE, MAE, as well as computation time. The improvement ratio showed 14.93% increase when dimension-reduced
dataset and normalized raw data were combined in comparison with the predicted case tested by using only normalized raw data. This study
could support optimum building energy operation planning and design by promoting the creation and implementation of energy-efficient smart
grid systems in the future. |