Recent advances in data-driven prediction for wind power DOI Creative Commons
Yaxin Liu, Yunjing Wang, Qingtian Wang

et al.

Frontiers in Energy Research, Journal Year: 2023, Volume and Issue: 11

Published: June 12, 2023

Wind power is one of the most representative renewable energy and has attracted wide attention in recent years. With increasing installed capacity global wind power, its nature randomness uncertainty posed a serious risk to safe stable operation system. Therefore, accurate prediction plays an increasingly important role controlling impact fluctuations system dispatch planning. Recently, with rapid accumulation data resource continuous improvement computing data-driven artificial intelligence technology been popularly applied many industries. AI-based models field have become cutting-edge research subject. This paper comprehensively reviews for at various temporal spatial scales, covering from turbine level regional level. To obtain in-depth insights on performance methods, we review analyze evaluation metrics both deterministic probabilistic prediction. In addition, challenges arising quality control, feature engineering, model generalization methods are discussed. Future directions improving accuracy also addressed.

Language: Английский

Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks DOI
Shilin Sun, Yuekai Liu, Qi Li

et al.

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 283, P. 116916 - 116916

Published: March 16, 2023

Language: Английский

Citations

101
Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model DOI

Sheng-Xiang Lv,

Lin Wang

Energy, Journal Year: 2022, Volume and Issue: 263, P. 126100 - 126100

Published: Nov. 14, 2022

Language: Английский

Citations

81
Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model DOI

Dongdong Zhang,

Baian Chen,

Hongyu Zhu

et al.

Energy, Journal Year: 2023, Volume and Issue: 285, P. 128762 - 128762

Published: Aug. 14, 2023

Language: Английский

Citations

72
A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction DOI Open Access
Jianzhou Wang, Yuansheng Qian,

Linyue Zhang

et al.

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 299, P. 117818 - 117818

Published: Nov. 16, 2023

Language: Английский

Citations

42
A new paradigm based on Wasserstein Generative Adversarial Network and time-series graph for integrated energy system forecasting DOI
Zhirui Tian, Gai Mei

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 326, P. 119484 - 119484

Published: Jan. 13, 2025

Language: Английский

Citations

2
A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network DOI
Tianhong Liu, Shengli Qi,

Xianzhu Qiao

et al.

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129904 - 129904

Published: Dec. 6, 2023

Language: Английский

Citations

28
Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level DOI

Mohammad Ehteram,

Elham Ghanbari-Adivi

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(40), P. 92903 - 92921

Published: July 27, 2023

Language: Английский

Citations

23
Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy DOI
Xiaodi Wang, Hao Yan,

Wendong Yang

et al.

Energy, Journal Year: 2024, Volume and Issue: 297, P. 131142 - 131142

Published: April 3, 2024

Language: Английский

Citations

14
An advanced airport terminal cooling load forecasting model integrating SSA and CNN-Transformer DOI

Bochao Chen,

Wansheng Yang,

Biao Yan

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 309, P. 114000 - 114000

Published: Feb. 21, 2024

Language: Английский

Citations

11
A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning DOI Creative Commons
Yan Chen,

Miaolin Yu,

Haochong Wei

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 580 - 580

Published: Jan. 26, 2025

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving utilization. However, real-world time series exhibit dynamic statistical properties, such as changing mean variance over time, which make it difficult models to apply observed patterns from the past future. Additionally, execution speed high computational resource demands of complex prediction them deploy on edge computing nodes farms. To address these issues, this paper explores potential linear constructs NFLM, a linear, lightweight, short-term model that more adapted characteristics data. The captures both long-term sequence variations through continuous interval sampling. mitigate interference features, we propose normalization feature learning block (NFLBlock) core component NFLM processing sequences. This module normalizes input data uses stacked multilayer perceptron extract cross-temporal cross-dimensional dependencies. Experiments with two real farms in Guangxi, China, showed compared other advanced methods, MSE 24-step ahead respectively reduced by 23.88% 21.03%, floating-point operations (FLOPs) parameter count only require 36.366 M 0.59 M, respectively. results show can achieve good accuracy fewer resources.

Language: Английский

Citations

1