Wind Power Forecasting

Wind power forecasting aims to accurately predict wind power generation, crucial for grid stability and efficient energy management in a renewable-energy-focused world. Current research emphasizes improving forecast accuracy and reliability across various timescales (from minutes to decades), employing diverse machine learning models such as deep neural networks (including CNNs, RNNs, and Transformers), Gaussian processes, and ensemble methods, often incorporating spatial and temporal dependencies. These advancements are vital for optimizing grid operations, energy trading, and renewable energy integration, contributing significantly to the transition towards sustainable energy systems.

Papers

October 26, 2024

DeepMIDE: A Multivariate Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting
Feng Ye, Xinxi Zhang, Michael Stein, Ahmed Aziz Ezzat
Deep Learning Spatio Temporal Multivariate Approach Univariate Time Series Forecasting Wind Power Forecasting Offshore Wind

September 10, 2024

Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes
Qiqi Li, Mike Ludkovski
Gaussian Process Spatiotemporal Modeling Day Ahead Wind Power Forecasting

August 27, 2024

Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6
Nina Effenberger, Nicole Ludwig
Wind Power Wind Power Forecasting Climate Model Output Coupled Model Intercomparison Project

June 21, 2024

Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference
Carlos Sebastián, Carlos E. González-Guillén, Jesús Juan
Prediction Interval Conformal Inference Wind Power Forecasting Heteroscedastic Regression Electricity Price Forecasting Point Forecasting

February 22, 2024

WindDragon: Enhancing wind power forecasting with Automated Deep Learning
Julie Keisler, Etienne Le Naour
Numerical Weather Prediction Forecasting Performance Wind Power Wind Power Forecasting Six DRAGON Fly Automated Deep Learning

February 21, 2024

Bias correction of wind power forecasts with SCADA data and continuous learning
Stefan Jonas, Kevin Winter, Bernhard Brodbeck, Angela Meyer
Numerical Weather Prediction Wind Power Continuous Learning Bias Correction Wind Power Forecasting

January 16, 2024

Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness
Mulomba Mukendi Christian, Yun Seon Kim, Hyebong Choi, Jaeyoung Lee, SongHee You
Deep Learning Native Robustness Long Horizon Accuracy Improvement Wind Speed CNN LSTM Shape Feature Wind Power Forecasting Wind Energy

December 25, 2023

Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Birgitte Bak-Jensen, Zhe Yang, Gonghao Zhang
Neural Network Inherent Interpretability Wind Power Consumption Forecasting Wind Power Forecasting Wind Data

October 28, 2023

Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Birgitte Bak-Jensen, Guangchun Ruan, Zhe Yang
Forecasting Model Explainable Model High Accuracy Wind Power Forecasting Glass Box

September 28, 2023

Asset Bundling for Wind Power Forecasting
Hanyu Zhang, Mathieu Tanneau, Chaofan Huang, V. Roshan Joseph, Shangkun Wang, Pascal Van Hentenryck
Wind Power Renewable Generation Wind Power Forecasting Hierarchical Time Series Bundle Sale Data

July 18, 2023

Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022
Longxing Tan, Hongying Yue
Application Proficiency BERT Model Solution Path Ticket BERT Forecasting Performance Wind Power Wind Power Forecasting KDD Cup

May 30, 2023

Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer
Yang Zhang, Lingbo Liu, Xinyu Xiong, Guanbin Li, Guoli Wang, Liang Lin
Wind Power Forecasting Hierarchical Spatial Temporal Transformer

May 19, 2023

Enhancing Short-Term Wind Speed Forecasting using Graph Attention and Frequency-Enhanced Mechanisms
Hao Liu, Huimin Ma, Tianyu Hu
Graph Attention Power Grid Wind Speed Wind Power Forecasting Frequency Enhancement Wind Speed Forecasting

March 29, 2023

Targeted Adversarial Attacks on Wind Power Forecasts
René Heinrich, Christoph Scholz, Stephan Vogt, Malte Lehna
Adversarial Attack Adversarial Robustness Long Short Term Memory Wind Power Wind Turbine Wind Power Forecasting

February 22, 2023

BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022
Jiawei Jiang, Chengkai Han, Jingyuan Wang
Temporal Graph Neural Network Wind Power Wind Power Forecasting KDD Cup

February 2, 2023

A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms
Meiyu Jiang, Jun Shen, Xuetao Jiang, Lihui Luo, Rui Zhou, Qingguo Zhou
Long Form Novel Temporal Attention Wind Power Wind Turbine Time Scale Wind Power Forecasting

January 2, 2023

A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting
Syed Kazmi, Berk Gorgulu, Mucahit Cevik, Mustafa Gokce Baydogan
Recurrent Neural Network Numerical Weather Prediction RNN Architecture Global Forecasting Wind Power Forecasting Concurrent Transmission

August 18, 2022

KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution
Fangquan Lin, Wei Jiang, Hanwei Zhang, Cheng Yang
Recurrent Neural Network Decision Tree Wind Power Wind Power Forecasting KDD Cup

August 8, 2022

SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022
Jingbo Zhou, Xinjiang Lu, Yixiong Xiao, Jiantao Su, Junfu Lyu, Yanjun Ma, Dejing Dou
Data Set Wind Power Wind Power Forecasting KDD Cup

July 8, 2022

AI-based Optimal scheduling of Renewable AC Microgrids with bidirectional LSTM-Based Wind Power Forecasting
Hossein Mohammadi, Shiva Jokar, Mojtaba Mohammadi, Abdollah Kavousifard, Morteza Dabbaghjamanesh
DC Microgrid Application Wind Power Forecasting Optimal Scheduling

Wind Power Forecasting

Papers

DeepMIDE: A Multivariate Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting

Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes

Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6

Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference

WindDragon: Enhancing wind power forecasting with Automated Deep Learning

Bias correction of wind power forecasts with SCADA data and continuous learning

Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness

Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting

Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy

Asset Bundling for Wind Power Forecasting

Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022

Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer

Enhancing Short-Term Wind Speed Forecasting using Graph Attention and Frequency-Enhanced Mechanisms

Targeted Adversarial Attacks on Wind Power Forecasts

BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022

A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms

A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting

KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution

SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

AI-based Optimal scheduling of Renewable AC Microgrids with bidirectional LSTM-Based Wind Power Forecasting