Open AccessAttention-Based Encoder-Decoder Model for Photovoltaic Power Generation Prediction
Author(s) -
Zhu Xiang,
Jicheng Hu,
Liangcai Song,
Guilong Suo,
Zhan Yong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1575/1/012025
Subject(s) - photovoltaic system , computer science , encoder , power (physics) , electronic engineering , electricity generation , engineering , electrical engineering , physics , quantum mechanics , operating system
The weather factors that affect the output of photovoltaic power generation systems have great volatility and discontinuities. Thus how to accurately predict the output of photovoltaic power generation has become a crucial issue. In this paper, we propose an attention-based Encoder-Decoder model for photovoltaic power generation. Filtered data based on maximum information coefficient is used as one of the features to reconstruct the experiment data. Then the attention mechanism is introduced to the Encoder-Decoder model, which constructed by Long Short-Term Memory (LSTM) neurons. We implement this experiment based on actual photovoltaic power plant examples and experimental results confirm the accuracy and applicability of the proposed model in predicting photovoltaic power generation