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Jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network
https://ipsj.ixsq.nii.ac.jp/records/235608
https://ipsj.ixsq.nii.ac.jp/records/23560867181fda-41c1-4439-9ac9-6c76339eac90
名前 / ファイル | ライセンス | アクション |
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2026年7月15日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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非会員:¥660, IPSJ:学会員:¥330, MPS:会員:¥0, DLIB:会員:¥0 |
Item type | SIG Technical Reports(1) | |||||||||
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公開日 | 2024-07-15 | |||||||||
タイトル | ||||||||||
タイトル | Jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network | |||||||||
タイトル | ||||||||||
言語 | en | |||||||||
タイトル | Jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network | |||||||||
言語 | ||||||||||
言語 | eng | |||||||||
資源タイプ | ||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
資源タイプ | technical report | |||||||||
著者所属 | ||||||||||
Hokkaido University | ||||||||||
著者所属 | ||||||||||
Hokkaido University | ||||||||||
著者所属(英) | ||||||||||
en | ||||||||||
Hokkaido University | ||||||||||
著者所属(英) | ||||||||||
en | ||||||||||
Hokkaido University | ||||||||||
著者名 |
Xingbang, Du
× Xingbang, Du
× Enzhi, Zhang
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著者名(英) |
Xingbang, Du
× Xingbang, Du
× Enzhi, Zhang
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論文抄録 | ||||||||||
内容記述タイプ | Other | |||||||||
内容記述 | Renewable energy generation forecasting plays crucial roles in advanced smart grid and sustainable practices. Although many RNN related methods have been utilized to predict power generation time series data, they often struggle to capture very long-term correlations efficiently due to the vanishing gradient issue. To address this challenge, we have introduced the Ultra long term network model that incorporated LSTM , SKIP LSTM and Dense components. This model effectively captures long-term patterns while mitigating the vanishing gradient problem associated with capturing very long term patterns. Our application of this model to renewable power prediction has yielded better performance when compared through metrics like MSE and MAE than previous models such as LSTM, GRU and Simple RNN models in time series analysis within smart grids. The integration of this model holds promise for enhancing the intelligence of renewable energy grids. | |||||||||
論文抄録(英) | ||||||||||
内容記述タイプ | Other | |||||||||
内容記述 | Renewable energy generation forecasting plays crucial roles in advanced smart grid and sustainable practices. Although many RNN related methods have been utilized to predict power generation time series data, they often struggle to capture very long-term correlations efficiently due to the vanishing gradient issue. To address this challenge, we have introduced the Ultra long term network model that incorporated LSTM , SKIP LSTM and Dense components. This model effectively captures long-term patterns while mitigating the vanishing gradient problem associated with capturing very long term patterns. Our application of this model to renewable power prediction has yielded better performance when compared through metrics like MSE and MAE than previous models such as LSTM, GRU and Simple RNN models in time series analysis within smart grids. The integration of this model holds promise for enhancing the intelligence of renewable energy grids. | |||||||||
書誌レコードID | ||||||||||
収録物識別子タイプ | NCID | |||||||||
収録物識別子 | AN10505667 | |||||||||
書誌情報 |
研究報告数理モデル化と問題解決(MPS) 巻 2024-MPS-149, 号 11, p. 1-4, 発行日 2024-07-15 |
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ISSN | ||||||||||
収録物識別子タイプ | ISSN | |||||||||
収録物識別子 | 2188-8833 | |||||||||
Notice | ||||||||||
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
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言語 | ja | |||||||||
出版者 | 情報処理学会 |