Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries from ICEEMDAN to the transformer network. Each transformer network computes the forecast subseries and then passes to the fusion phase. Get the primary wind speed forecasting from the fusion of individual transformer network forecast subseries. Estimate the residual error values and predict errors using a multilayer perceptron neural network. The forecast error is added to the primary forecast wind speed to leverage the high accuracy of wind speed forecasting. Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06, MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1 and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08, MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.
Spatiotemporal Attention Networks for Wind Power Forecasting
System Engineering Institute, Xi’an Jiaotong University（西安交通大学系统工程研究所）；Rutgers University（罗格斯大学）；Electric Power Research Institute, State Grid Liaoning Electric Power Co. Ltd.（国网辽宁省电力有限公司电力科学研究院）
A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms
Lanzhou University（兰州大学）；University of Wollongong（伍伦贡大学）；Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院西北生态环境资源研究院）
TCNFormer: Temporal Convolutional Network Former for Short-Term Wind Speed Forecasting
Aligarh Muslim University（阿里格尔穆斯林大学）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；Monash University Malaysia（莫纳什大学马来西亚分校）；Tibet University（西藏大学）；Shanghai University（上海大学）
2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China（人工智能学院，河北工业大学，天津，中国）；State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China（电力设备可靠性与智能化国家重点实验室，河北工业大学，天津，中国）；Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China（河北省大数据计算重点实验室，河北工业大学，天津，中国）
Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring
Department of Mechanical Engineering, University of Bath（巴斯大学机械工程系）；Department of Civil Engineering, Zhejiang University（浙江大学土木工程系）
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm
The University of Adelaide（阿德莱德大学）；Sapienza University of Rome（罗马大学）；The Australian Institute for Machine Learning（澳大利亚机器学习研究所）；KTH Royal Institute of Technology（瑞典皇家理工学院）
