2026-03-15T12:06:04Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00235608 2025-01-19T09:35:37Z 1164:2735:11468:11702

Jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network Jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network Xingbang, Du Enzhi, Zhang Xingbang, Du Enzhi, Zhang 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. 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. technical report 情報処理学会 2024-07-15 application/pdf 研究報告数理モデル化と問題解決（MPS） 11 2024-MPS-149 1 4 2188-8833 AN10505667 https://ipsj.ixsq.nii.ac.jp/record/235608/files/IPSJ-MPS24149011.pdf eng