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- W4386323483 abstract "Photovoltaic (PV) is a clean and renewable energy source and is considered an effective way to solve energy problems and reduce emissions. However, photovoltaic output is significantly impacted by different weather conditions. To improve the reliability and stability of photovoltaic resource, in this paper, we propose a photovoltaic output prediction approach to accurately deduct the future photovoltaic output capability. First, we construct a weighted undirected spatio-temporal graph to characterize the relationship between distributed photovoltaic sites. Then, we utilize the LSTM network to study time-series features of each node in the spatio-temporal graph. After that, we jointly design the edge and feature decoding architecture to learn the spatio-temporal features of nodes, based on which a multi-layer fully connected network is used to effectively infer the photovoltaic output of on-site nodes. Experimental results show that our proposed model outperforms other state-of-the-art models." @default.
- W4386323483 created "2023-09-01" @default.
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- W4386323483 date "2023-07-17" @default.
- W4386323483 modified "2023-09-27" @default.
- W4386323483 title "A Spatio-temporal Graph Based Hybrid Encoder-Decoder Approach for PV Output Prediction" @default.
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- W4386323483 doi "https://doi.org/10.1109/icce-taiwan58799.2023.10227013" @default.
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