LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, thi...

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Bibliographic Details
Main Authors: Tongguang Yang, Bin Li, Qian Xun
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
LSTM-attention-embedding model
features extraction
deep learning
Bayesian optimization
residual connection
Online Access:https://ieeexplore.ieee.org/document/8908794/

Internet

https://ieeexplore.ieee.org/document/8908794/

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