@article {31665,
	title = {Incorporating machine learning with building network analysis to predict multi-building energy use},
	journal = {Energy and Buildings},
	volume = {186},
	year = {2019},
	month = {06/2019},
	pages = {80 - 97},
	abstract = {<p>Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67\% for the office group, 90.79\% for the educational group, 92.34\% for the laboratory group, and 83.32\% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28\% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.</p>},
	keywords = {Artificial neural networks, Building network, cold winter and hot summer climate, Energy use prediction, Machine learning},
	issn = {03787788},
	doi = {10.1016/j.enbuild.2019.01.002},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818319765},
	author = {Xiaodong Xu and Wei Wang and Tianzhen Hong and Jiayu Chen}
}
