Research graph
References from Frequency-enhanced dual-graph modeling with reinforcement learning for wind power forecasting. Local targets link to admitted publications; unresolved targets remain external evidence.
STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting
10.1016/j.apenergy.2024.124034 · 2024 · External reference
A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies
10.1016/j.eswa.2023.122487 · 2024 · External reference
Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm
10.1016/j.chaos.2023.114222 · 2023 · External reference
Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN
10.1016/j.energy.2024.131448 · 2024 · External reference
Research on the short-term wind power prediction with dual branch multi-source fusion strategy
10.1016/j.energy.2024.130402 · 2024 · External reference
A novel wind speed prediction method based on fractal wavelet decomposition explainable gated recurrent unit
10.1016/j.chaos.2025.116975 · 2025 · External reference
A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting
10.1016/j.apenergy.2022.118777 · 2022 · External reference
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
10.1016/j.engappai.2023.105982 · 2023 · External reference
Frequency-aware ultra-short-term wind power forecasting using CEEMDAN-VMD-SE and transformer-GRU networks
10.1016/j.energy.2025.138715 · 2025 · External reference
A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic
10.1016/j.enconman.2019.112254 · 2020 · External reference
Fault diagnosis for rolling bearing based on VMD-FRFT
10.1016/j.measurement.2020.107554 · 2020 · External reference
Short-time variational mode decomposition
10.1016/j.sigpro.2025.110203 · 2026 · External reference
Unresolved reference
2022 · External reference
A review of wind speed and wind power forecasting with deep neural networks
10.1016/j.apenergy.2021.117766 · 2021 · External reference
Unresolved reference
2021 · External reference
Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method
10.1016/j.apenergy.2022.119579 · 2022 · External reference
A spatiotemporal wind power forecasting method based on dual-view graph fusion and dual-granularity residual learning
10.1016/j.neunet.2026.108923 · 2026 · External reference
A hybrid error correction method based on EEMD and ConvLSTM for offshore wind power forecasting
10.1016/j.oceaneng.2025.120773 · 2025 · External reference
Short-term wind power prediction based on extreme learning machine with error correction
10.1186/s41601-016-0016-y · 2016 · External reference
A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy
10.1016/j.egyr.2022.07.080 · 2022 · External reference
An improved residual-based convolutional neural network for very short-term wind power forecasting
10.1016/j.enconman.2020.113731 · 2021 · External reference
Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting: MHDM Ribeiro et al.
10.1007/s10489-024-05331-2 · 2024 · External reference
A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM
10.1016/j.renene.2020.09.078 · 2021 · External reference
Physics-constrained generative augmentation and hierarchical decomposition driven wind power forecasting framework under extreme weather conditions
10.1016/j.energy.2026.141322 · 2026 · External reference
Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting
10.1109/tste.2020.3043884 · 2020 · External reference
An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model
10.1016/j.energy.2024.130751 · 2024 · External reference
Heterogeneous spatiotemporal graph convolution network for multi-modal wind-PV power collaborative prediction
10.1109/tpwrs.2023.3342636 · 2023 · External reference
GWO-ANFIS-RD3PG: a reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting
2024 · External reference
A novel deep reinforcement learning model based on DDPG considering attention mechanism and combined with GRU network for short-term load forecasting
2025 · External reference
Predictive deep reinforcement learning with multi-agent systems for adaptive time series forecasting
10.1016/j.knosys.2025.113941 · 2025 · External reference
Wind power forecasting considering data privacy protection: a federated deep reinforcement learning approach
10.1016/j.apenergy.2022.120291 · 2023 · External reference
Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach
10.1016/j.energy.2021.122128 · 2022 · External reference
Explainable AI for wind energy systems: state-of-the-art techniques, challenges, and future directions
2025 · External reference
Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
10.1016/j.eswa.2023.121464 · 2024 · External reference
Interpretability research of deep learning: a literature survey
10.1016/j.inffus.2024.102721 · 2025 · External reference
Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables
10.1016/j.apenergy.2023.122155 · 2024 · External reference
Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction
10.1016/j.energy.2023.129640 · 2024 · External reference
STE-HOLNet: a new method for wind power prediction by integrating spatio-temporal features, dynamic concept drift detection and adaptive correction
10.1016/j.enconman.2025.120138 · 2025 · External reference
A novel wind power prediction model improved with feature enhancement and autoregressive error compensation
10.1016/j.jclepro.2023.138386 · 2023 · External reference
Wind power prediction based on improved self-attention mechanism combined with Bi-directional temporal convolutional network
10.1016/j.energy.2025.135666 · 2025 · External reference
Integrating signal pairing evaluation metrics with deep learning for wind power forecasting through coupled multiple modal decomposition and aggregation
10.1016/j.knosys.2025.113394 · 2025 · External reference
Data-augmented trend-fluctuation representations by interpretable contrastive learning for wind power forecasting
10.1016/j.apenergy.2024.125052 · 2025 · External reference
Multifactor interpretability method for offshore wind power output prediction based on TPE-CatBoost-SHAP
10.1016/j.compeleceng.2025.110081 · 2025 · External reference
Explainable artificial intelligence for wind power forecasting model based on long short-term memory
2025 · External reference
A short-term wind power prediction method via self-adaptive adjacency matrix and spatiotemporal graph neural networks
10.1016/j.compeleceng.2024.109715 · 2024 · External reference
Interpretable multi-graph convolution network integrating spatio-temporal attention and dynamic combination for wind power forecasting
10.1016/j.eswa.2024.124766 · 2024 · External reference
Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention
10.1016/j.apenergy.2022.119288 · 2022 · External reference
MDHGFN: multiscale dual hypergraph fusion spatiotemporal network for traffic flow prediction
10.1016/j.chaos.2025.117228 · 2025 · External reference
A novel hybrid deep learning model for multi-step wind speed forecasting considering pairwise dependencies among multiple atmospheric variables
10.1016/j.energy.2023.129408 · 2023 · External reference
Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting
10.1016/j.renene.2021.08.066 · 2021 · External reference
Short-term wind power forecasting based on attention mechanism and deep learning
10.1016/j.epsr.2022.107776 · 2022 · External reference
A hybrid attention-based deep learning approach for wind power prediction
10.1016/j.apenergy.2022.119608 · 2022 · External reference
Multivariate solar power time series forecasting using multilevel data fusion and deep neural networks
10.1016/j.inffus.2023.102180 · 2024 · External reference
Convolutional neural networks on graphs with fast localized spectral filtering
2016 · External reference
Enhancing PV power forecasting accuracy through nonlinear weather correction based on multi-task learning
10.1016/j.apenergy.2025.125525 · 2025 · External reference
Graph-enabled reinforcement learning for time series forecasting with adaptive intelligence
10.1109/tetci.2024.3398024 · 2024 · External reference
Continuous control with deep reinforcement learning
2016 · External reference
Solar and wind power data from the Chinese state grid renewable energy generation forecasting competition
10.1038/s41597-022-01696-6 · 2022 · External reference
A case study on a hybrid wind speed forecasting method using BP neural network
10.1016/j.knosys.2011.04.019 · 2011 · External reference
Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model
10.1016/j.energy.2023.128762 · 2023 · External reference
An ALBERT-based TextCNN-Hatt hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters
10.1016/j.engappai.2023.106136 · 2023 · External reference
A survey on graph neural networks for time series: forecasting, classification, imputation, and anomaly detection
10.1109/tpami.2024.3443141 · 2024 · External reference
Unresolved reference
2021 · External reference
Photovoltaic power forecasting based on VMD-SSA-transformer: multidimensional analysis of dataset length, weather mutation and forecast accuracy
10.1016/j.energy.2025.135971 · 2025 · External reference
Multi-scale patch and frequency-domain gated learning for high-resolution day-ahead photovoltaic forecasting
10.1016/j.apenergy.2025.126973 · 2026 · External reference
Wind power forecasting: a hybrid multi-layer perceptron framework with adaptive noise reduction and error correction
10.1016/j.compeleceng.2025.110689 · 2025 · External reference
On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers
1927 · External reference
Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting: MHDM Ribeiro et al.
10.1007/s10489-024-05331-2 · ExternalCitation · doi-reference
A review of wind speed and wind power forecasting with deep neural networks
10.1016/j.apenergy.2021.117766 · ExternalCitation · doi-reference
A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting
10.1016/j.apenergy.2022.118777 · ExternalCitation · doi-reference
Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention
10.1016/j.apenergy.2022.119288 · ExternalCitation · doi-reference
Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method
10.1016/j.apenergy.2022.119579 · ExternalCitation · doi-reference
A hybrid attention-based deep learning approach for wind power prediction
10.1016/j.apenergy.2022.119608 · ExternalCitation · doi-reference
Wind power forecasting considering data privacy protection: a federated deep reinforcement learning approach
10.1016/j.apenergy.2022.120291 · ExternalCitation · doi-reference
Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables
10.1016/j.apenergy.2023.122155 · ExternalCitation · doi-reference
STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting
10.1016/j.apenergy.2024.124034 · ExternalCitation · doi-reference
Data-augmented trend-fluctuation representations by interpretable contrastive learning for wind power forecasting
10.1016/j.apenergy.2024.125052 · ExternalCitation · doi-reference
Enhancing PV power forecasting accuracy through nonlinear weather correction based on multi-task learning
10.1016/j.apenergy.2025.125525 · ExternalCitation · doi-reference
Multi-scale patch and frequency-domain gated learning for high-resolution day-ahead photovoltaic forecasting
10.1016/j.apenergy.2025.126973 · ExternalCitation · doi-reference
Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm
10.1016/j.chaos.2023.114222 · ExternalCitation · doi-reference
A novel wind speed prediction method based on fractal wavelet decomposition explainable gated recurrent unit
10.1016/j.chaos.2025.116975 · ExternalCitation · doi-reference
MDHGFN: multiscale dual hypergraph fusion spatiotemporal network for traffic flow prediction
10.1016/j.chaos.2025.117228 · ExternalCitation · doi-reference
A short-term wind power prediction method via self-adaptive adjacency matrix and spatiotemporal graph neural networks
10.1016/j.compeleceng.2024.109715 · ExternalCitation · doi-reference
Multifactor interpretability method for offshore wind power output prediction based on TPE-CatBoost-SHAP
10.1016/j.compeleceng.2025.110081 · ExternalCitation · doi-reference
Wind power forecasting: a hybrid multi-layer perceptron framework with adaptive noise reduction and error correction
10.1016/j.compeleceng.2025.110689 · ExternalCitation · doi-reference
A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy
10.1016/j.egyr.2022.07.080 · ExternalCitation · doi-reference
A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic
10.1016/j.enconman.2019.112254 · ExternalCitation · doi-reference
An improved residual-based convolutional neural network for very short-term wind power forecasting
10.1016/j.enconman.2020.113731 · ExternalCitation · doi-reference
STE-HOLNet: a new method for wind power prediction by integrating spatio-temporal features, dynamic concept drift detection and adaptive correction
10.1016/j.enconman.2025.120138 · ExternalCitation · doi-reference
Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach
10.1016/j.energy.2021.122128 · ExternalCitation · doi-reference
Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model
10.1016/j.energy.2023.128762 · ExternalCitation · doi-reference
A novel hybrid deep learning model for multi-step wind speed forecasting considering pairwise dependencies among multiple atmospheric variables
10.1016/j.energy.2023.129408 · ExternalCitation · doi-reference
Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction
10.1016/j.energy.2023.129640 · ExternalCitation · doi-reference
Research on the short-term wind power prediction with dual branch multi-source fusion strategy
10.1016/j.energy.2024.130402 · ExternalCitation · doi-reference
An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model
10.1016/j.energy.2024.130751 · ExternalCitation · doi-reference
Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN
10.1016/j.energy.2024.131448 · ExternalCitation · doi-reference
Wind power prediction based on improved self-attention mechanism combined with Bi-directional temporal convolutional network
10.1016/j.energy.2025.135666 · ExternalCitation · doi-reference
Photovoltaic power forecasting based on VMD-SSA-transformer: multidimensional analysis of dataset length, weather mutation and forecast accuracy
10.1016/j.energy.2025.135971 · ExternalCitation · doi-reference
Frequency-aware ultra-short-term wind power forecasting using CEEMDAN-VMD-SE and transformer-GRU networks
10.1016/j.energy.2025.138715 · ExternalCitation · doi-reference
Physics-constrained generative augmentation and hierarchical decomposition driven wind power forecasting framework under extreme weather conditions
10.1016/j.energy.2026.141322 · ExternalCitation · doi-reference
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
10.1016/j.engappai.2023.105982 · ExternalCitation · doi-reference
An ALBERT-based TextCNN-Hatt hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters
10.1016/j.engappai.2023.106136 · ExternalCitation · doi-reference
Short-term wind power forecasting based on attention mechanism and deep learning
10.1016/j.epsr.2022.107776 · ExternalCitation · doi-reference
Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
10.1016/j.eswa.2023.121464 · ExternalCitation · doi-reference
A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies
10.1016/j.eswa.2023.122487 · ExternalCitation · doi-reference
Interpretable multi-graph convolution network integrating spatio-temporal attention and dynamic combination for wind power forecasting
10.1016/j.eswa.2024.124766 · ExternalCitation · doi-reference
Multivariate solar power time series forecasting using multilevel data fusion and deep neural networks
10.1016/j.inffus.2023.102180 · ExternalCitation · doi-reference
Interpretability research of deep learning: a literature survey
10.1016/j.inffus.2024.102721 · ExternalCitation · doi-reference
A novel wind power prediction model improved with feature enhancement and autoregressive error compensation
10.1016/j.jclepro.2023.138386 · ExternalCitation · doi-reference
A case study on a hybrid wind speed forecasting method using BP neural network
10.1016/j.knosys.2011.04.019 · ExternalCitation · doi-reference
Integrating signal pairing evaluation metrics with deep learning for wind power forecasting through coupled multiple modal decomposition and aggregation
10.1016/j.knosys.2025.113394 · ExternalCitation · doi-reference
Predictive deep reinforcement learning with multi-agent systems for adaptive time series forecasting
10.1016/j.knosys.2025.113941 · ExternalCitation · doi-reference
Fault diagnosis for rolling bearing based on VMD-FRFT
10.1016/j.measurement.2020.107554 · ExternalCitation · doi-reference
A spatiotemporal wind power forecasting method based on dual-view graph fusion and dual-granularity residual learning
10.1016/j.neunet.2026.108923 · ExternalCitation · doi-reference
A hybrid error correction method based on EEMD and ConvLSTM for offshore wind power forecasting
10.1016/j.oceaneng.2025.120773 · ExternalCitation · doi-reference
A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM
10.1016/j.renene.2020.09.078 · ExternalCitation · doi-reference
Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting
10.1016/j.renene.2021.08.066 · ExternalCitation · doi-reference
Short-time variational mode decomposition
10.1016/j.sigpro.2025.110203 · ExternalCitation · doi-reference
Solar and wind power data from the Chinese state grid renewable energy generation forecasting competition
10.1038/s41597-022-01696-6 · ExternalCitation · doi-reference
Graph-enabled reinforcement learning for time series forecasting with adaptive intelligence
10.1109/tetci.2024.3398024 · ExternalCitation · doi-reference
A survey on graph neural networks for time series: forecasting, classification, imputation, and anomaly detection
10.1109/tpami.2024.3443141 · ExternalCitation · doi-reference
Heterogeneous spatiotemporal graph convolution network for multi-modal wind-PV power collaborative prediction
10.1109/tpwrs.2023.3342636 · ExternalCitation · doi-reference
Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting
10.1109/tste.2020.3043884 · ExternalCitation · doi-reference
Short-term wind power prediction based on extreme learning machine with error correction
10.1186/s41601-016-0016-y · ExternalCitation · doi-reference
