Urban building energy prediction at neighborhood scale

Urban building energy model has been the focus of much research in recent years, especially using data driven techniques, however, the success of which needs to solve the recognized challenges, such as sufficient energy use dataset in spatial and temporal scales and mutual effect between inter-buildings. Using monthly and yearly energy data from 539 residential buildings and 153 public buildings in a county-level city, this study investigated five typical data-driven urban building energy prediction models on the neighborhood scale. The k nearest neighbors (KNN), support vector regression (SVR), and long shortterm memory (LSTM) algorithms were selected as data-driven predictive techniques. The Model 1 was a data-driven energy prediction model for individual building, and with LSTM, the best results for Model 1 can be averagely 0.41 of MAPE and averagely 0.57 of R2. The Model 2 applied different percentages (100%, 95%, 90%, 85%, 80%, 75%, 70%, 65%, and 60%) of original energy dataset to predict total energy demand. The results for Model 2 are averagely 0.065 of MAPE and averagely 0.95 of R2, which also proved that reducing the size of dataset did not influence the results. The Model 3 and 4 created building networks with energy data and building morphology, respectively, and integrated them in urban building energy prediction models, The MAPE results are mostly lower than 0.4 and 0.36, respectively, and R2 results are mostly higher than 0.85 and 0.8 for Model 3 and 4, respectively. The Model 5 combined building morphological metrics and yearly energy data, which received 0.093 and 0.194 of MAPE results and 0.975 and 0.99 of R2 for residential and public buildings, respectively. Finally, this study can contribute to provide more solutions to urban building energy prediction while reduce the high data requirements of urban energy models. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Urban building energy model is a focus of recent research, with challenges like data requirements and inter-building effects. Five data-driven models were tested on various scales, with different algorithms and dataset sizes influencing energy prediction results. Integration of building networks and morphology improved prediction accuracy, showing potential for reducing data requirements in urban energy models.