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

Jieyang Peng, Andreas Kimmig, Jiahai Wang, Xiufeng Liu*, Zhibin Niu*, Jivka Ovtcharova

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.
Original languageEnglish
Article number111211
JournalEnergy and Buildings
Volume249
ISSN0378-7788
DOIs
Publication statusPublished - 2021

Keywords

  • Energy demand forecasting
  • Energy consumption pattern recognition
  • Long short-term memory network
  • Attention mechanism
  • Word embedding

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