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

Clicks: 195
ID: 268847
2021
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #155 of 220 articles by views in energies

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 220 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
jin2021energiesdeep-learning Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xue-Bo Jin;Wei-Zhen Zheng;Jian-Lei Kong;Xiao-Yi Wang;Yu-Ting Bai;Ting-Li Su;Seng Lin;Jin, Xue-Bo;Zheng, Wei-Zhen;Kong, Jian-Lei;Wang, Xiao-Yi;Bai, Yu-Ting;Su, Ting-Li;Lin, Seng;
Journal energies
Year 2021
DOI
10.3390/en14061596
URL
https://doi.org/10.3390/en14061596
Keywords
bayesian optimization electric power load prediction deep-learning encoder-decoder framework gated recurrent neural units temporal attention

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.