Urban building energy prediction at neighborhood scale

• Published in: Energy and buildings Vol. 251; p. 111307
• Main Authors: Wang, Wei, Lin, Qi, Chen, Jiayu, Li, Xiangfeng, Sun, Yiqiao, Xu, Xiaodong
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15-11-2021; Elsevier BV
• Subjects: Algorithms; Building network; Buildings; Data-driven model; Dataset requirement reduction; Datasets; Energy; Energy consumption; Energy demand; Long short-term memory; Morphology; Neighborhoods; Prediction models; Predictions; Public buildings; Residential areas; Residential buildings; Residential energy; Support vector machines; Urban areas; Urban building energy prediction; Long short-term memory; Dataset requirement reduction; Building network; Data-driven model; Urban building energy prediction
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2021.111307

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 short-term 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.