Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization

Xue-Bo Jin, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su and Seng Lin
Additional contact information
Xue-Bo Jin: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Wei-Zhen Zheng: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Jian-Lei Kong: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Xiao-Yi Wang: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Yu-Ting Bai: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Ting-Li Su: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Seng Lin: Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China

Energies, 2021, vol. 14, issue 6, 1-18

Abstract: Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

Keywords: electric power load prediction; deep-learning encoder-decoder framework; gated recurrent neural units; temporal attention; Bayesian optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/6/1596/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/6/1596/ (text/html)

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:gam:jeners:v:14:y:2021:i:6:p:1596-:d:516332

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().