A hybrid deep transfer learning strategy for short term cross-building energy prediction

Xi Fang, Guangcai Gong, Guannan Li, Liang Chun, Wenqiang Li and Pei Peng

Energy, 2021, vol. 215, issue PB

Abstract: To overcome the data shortage problem of model training in building energy prediction, this study proposes a novel hybrid deep transfer learning strategy for short term cross-building energy prediction using long short term memory (LSTM) and domain adversarial neural network (DANN). The proposed strategy can utilize knowledge learned from relevant building data to assist the energy prediction for target buildings with limited historical measurements. LSTM based feature extractor is used to extract temporal features across source and target buildings. DANN attempts to find domain invariant features between the source and target buildings via domain adaptation. Then, the domain adaptation based transfer learning model (i.e. LSTM-DANN) trained with data from source buildings can be applied to assist in predicting the target building energy without prediction performance degradation caused by domain shift. Experiments are conducted to evaluate the performance of the proposed transfer learning strategy in different models. Results demonstrate that the proposed strategy can significantly enhance the building energy prediction performance compared to models trained on the target only data, the source only data, both the target and source data, but without transfer learning. This work can provide guidance for the effective use of existing building data resources.

Keywords: Deep transfer learning strategy; Domain adaptation; Long short term memory; Domain adversarial neural network; Cross building; Energy prediction (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422032315X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:215:y:2021:i:pb:s036054422032315x

DOI: 10.1016/j.energy.2020.119208

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().