Implementing ultra-short-term wind power forecasting without information leakage through cascade decomposition and attention mechanism DOI
Jianguo Wang, Weiru Yuan, Shude Zhang

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133513 - 133513

Published: Oct. 1, 2024

Language: Английский

Multi-scale patch transformer with adaptive decomposition for carbon emissions forecasting DOI
Xiang Li, Lei Chu,

Yujun Li

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 146, P. 110153 - 110153

Published: Feb. 17, 2025

Language: Английский

Citations

0
High-throughput design and performance validation of superior latent heat eutectic salt materials DOI
Fengyi Yang,

Yimin Xuan,

Xianglei Liu

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 114, P. 115864 - 115864

Published: Feb. 17, 2025

Language: Английский

Citations

0
Enhancing solar radiation forecasting accuracy with a hybrid SA-Bi-LSTM-Bi-GRU model DOI

Girijapati Sharma,

Subhash Chandra,

Arvind Yadav

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 19, 2025

Language: Английский

Citations

0
Dynamic rolling horizon optimization for network-constrained V2X value stacking of electric vehicles under uncertainties DOI Creative Commons
Canchen Jiang, Ariel Liebman, Bo Jie

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122668 - 122668

Published: Feb. 1, 2025

Language: Английский

Citations

0
Data-driven deep learning model for short-term wind power prediction assisted with WGAN-GP data preprocessing DOI
Wei Wang, Jian Yang, Yihuan Li

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127068 - 127068

Published: Feb. 1, 2025

Language: Английский

Citations

0
A dual-dimensionality reduction attention mechanism with fusion of high-dimensional features for wind power prediction DOI
Liexi Xiao, Anbo Meng, Jiayu Rong

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122950 - 122950

Published: March 1, 2025

Language: Английский

Citations

0
Integrating signal pairing evaluation metrics with deep learning for wind power forecasting through coupled multiple modal decomposition and aggregation DOI
Yunbing Liu,

Jie Dai,

Guici Chen

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113394 - 113394

Published: April 1, 2025

Language: Английский

Citations

0
A TSFLinear model for wind power prediction with feature decomposition-clustering DOI
Huawei Mei, Qingyuan Zhu,

Cao Wangbin

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123142 - 123142

Published: April 1, 2025

Language: Английский

Citations

0
Maize yield estimation in Northeast China’s black soil region using a deep learning model with attention mechanism and remote sensing DOI Creative Commons
Xingke Li,

Yunfeng Lv,

Bingxue Zhu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 15, 2025

Abstract Accurate prediction of maize yields is crucial for effective crop management. In this paper, we propose a novel deep learning framework (CNNAtBiGRU) estimating yield, which applied to typical black soil areas in Northeast China. This integrates one-dimensional convolutional neural network (1D-CNN), bidirectional gated recurrent units (BiGRU), and an attention mechanism effectively characterize weight key segments input data. the predictions most recent year, model demonstrated high accuracy (R² = 0.896, RMSE 908.33 kg/ha) exhibited strong robustness both earlier years during extreme climatic events. Unlike traditional yield estimation methods that primarily rely on remote sensing vegetation indices, phenological data, meteorological characteristics, study innovatively incorporates anthropogenic factors, such as Degree Cultivation Mechanization (DCM), reflecting rapid advancement agricultural modernization. The relative importance analysis variables revealed Enhanced Vegetation Index (EVI), Sun-Induced Chlorophyll Fluorescence (SIF), DCM were influential factors prediction. Furthermore, our enables 1–2 months advance by leveraging historical patterns environmental variables, providing valuable lead time decision-making. predictive capability does not forecasting future weather conditions but rather captures yield-relevant signals embedded early-season

Language: Английский

Citations

0
Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction DOI Creative Commons
Yalong Li, Ye Jin, Yangqing Dan

et al.

IET Renewable Power Generation, Journal Year: 2024, Volume and Issue: 18(14), P. 2195 - 2208

Published: Feb. 1, 2024

Abstract Accurate estimation of wind power is essential for predicting and maintaining the balance in system. This paper proposes a novel approach to enhance accuracy through hybrid model integrating neural networks error discrimination‐correction techniques. In order improve estimation, bidirectional gating recurrent unit developed, forming an initial curve training. Additionally, sequential model‐based algorithmic configuration optimizes unit's network hyperparameters. To tackle errors, multi‐layer perceptron combined with employed create classification that automatically discerns quality estimates. Subsequently, innovative correction model, based on grey relevancy degree devised rectify erroneous The final estimates result from summation values derived corrections. By analysing real data farm northwest China, simulation test validates proposed model. Experimental results demonstrate substantial improvement modelling when compared

Language: Английский

Citations

3