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

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 f...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Energies (Basel) Ročník 14; číslo 6; s. 1596
Hlavní autori: Jin, Xue-Bo, Zheng, Wei-Zhen, Kong, Jian-Lei, Wang, Xiao-Yi, Bai, Yu-Ting, Su, Ting-Li, Lin, Seng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: MDPI AG 01.03.2021
Predmet:
Bayesian optimization
deep-learning encoder-decoder framework
electric power load prediction
gated recurrent neural units
temporal attention
ISSN:1996-1073, 1996-1073
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14061596