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Dual-stage attention-based long-short-term memory neural networks for energy demand prediction

Peng, Jieyang 1; Kimmig, Andreas 1; Wang, Jiahai; Liu, Xiufeng; Niu, Zhibin; Ovtcharova, Jivka 1
1 Karlsruher Institut für Technologie (KIT)

Abstract:

Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000138050
Veröffentlicht am 28.09.2021
Originalveröffentlichung
DOI: 10.1016/j.enbuild.2021.111211
Scopus
Zitationen: 23
Web of Science
Zitationen: 18
Dimensions
Zitationen: 25
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationsmanagement im Ingenieurwesen (IMI)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 10.2021
Sprache Englisch
Identifikator ISSN: 0378-7788
KITopen-ID: 1000138050
Erschienen in Energy and buildings
Verlag Elsevier
Band 249
Seiten Art.-Nr.: 111211
Vorab online veröffentlicht am 24.06.2021
Nachgewiesen in Dimensions
Scopus
Web of Science
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