TY - GEN
T1 - Synthetic minority oversampling based machine learning method for urban level building EUI prediction and benchmarking
AU - Jin, Xiaoyu
AU - Xiao, Fu
PY - 2021/9
Y1 - 2021/9
N2 - Machine learning holds a lot of promise for quickly and correctly assessing building energy performance at urban level. However, due to the lack of data for minority types of buildings, unfavorable results are produced sometimes. Therefore, this study proposes a concise approach to generate enough data for training machine learning models while avoiding overfitting. Superior results are obtained. The importance of variables is analyzed using urban open data sets, which are valuable to data collectors and publishers in decision-making.
AB - Machine learning holds a lot of promise for quickly and correctly assessing building energy performance at urban level. However, due to the lack of data for minority types of buildings, unfavorable results are produced sometimes. Therefore, this study proposes a concise approach to generate enough data for training machine learning models while avoiding overfitting. Superior results are obtained. The importance of variables is analyzed using urban open data sets, which are valuable to data collectors and publishers in decision-making.
U2 - 10.46855/energy-proceedings-8459
DO - 10.46855/energy-proceedings-8459
M3 - Conference article published in proceeding or book
T3 - Energy Proceedings
BT - The 7th Applied Energy Symposium 2021
T2 - Applied Energy Symposium 2021
Y2 - 4 September 2021 through 8 September 2021
ER -