IDEAS home Printed from https://ideas.repec.org/r/gam/jeners/v13y2020i15p3764-d387902.html
   My bibliography  Save this item

A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Yitian Xing & Fue-Sang Lien & William Melek & Eugene Yee, 2022. "A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model," Energies, MDPI, vol. 15(15), pages 1-35, July.
  2. Yang, Mao & Wang, Da & Zhang, Wei, 2024. "A novel ultra short-term wind power prediction model based on double model coordination switching mechanism," Energy, Elsevier, vol. 289(C).
  3. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
  4. Gong, Zhipeng & Wan, Anping & Ji, Yunsong & AL-Bukhaiti, Khalil & Yao, Zhehe, 2024. "Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model," Energy, Elsevier, vol. 295(C).
  5. Jingtao Huang & Gang Niu & Haiping Guan & Shuzhong Song, 2023. "Ultra-Short-Term Wind Power Prediction Based on LSTM with Loss Shrinkage Adam," Energies, MDPI, vol. 16(9), pages 1-13, April.
  6. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
  7. Qiu, Hong & Shi, Kaikai & Wang, Renfang & Zhang, Liang & Liu, Xiufeng & Cheng, Xu, 2024. "A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects," Renewable Energy, Elsevier, vol. 227(C).
  8. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
  9. Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
  10. Couto, António & Estanqueiro, Ana, 2022. "Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks," Renewable Energy, Elsevier, vol. 201(P1), pages 1076-1085.
  11. Yang, Mao & Wang, Da & Zhang, Wei, 2023. "A short-term wind power prediction method based on dynamic and static feature fusion mining," Energy, Elsevier, vol. 280(C).
  12. Keren Li & Sergey Utyuzhnikov, 2024. "Prediction of wind energy with the use of tensor‐train based higher order dynamic mode decomposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2434-2447, November.
  13. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
  14. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
  15. Yongnian Zhao & Yu Xue & Shanhong Gao & Jundong Wang & Qingcai Cao & Tao Sun & Yan Liu, 2022. "Computation and Analysis of an Offshore Wind Power Forecast: Towards a Better Assessment of Offshore Wind Power Plant Aerodynamics," Energies, MDPI, vol. 15(12), pages 1-17, June.
  16. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Kevin Tomsovic, 2022. "An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-20, September.
  17. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
  18. Yingya Zhou & Linwei Ma & Weidou Ni & Colin Yu, 2023. "Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting," Energies, MDPI, vol. 16(5), pages 1-18, February.
  19. Zhao, Beizhen & He, Xin & Ran, Shaolin & Zhang, Yong & Cheng, Cheng, 2024. "Spatial correlation learning based on graph neural network for medium-term wind power forecasting," Energy, Elsevier, vol. 296(C).
  20. Bowen Zhou & Zhibo Zhang & Guangdi Li & Dongsheng Yang & Matilde Santos, 2023. "Review of Key Technologies for Offshore Floating Wind Power Generation," Energies, MDPI, vol. 16(2), pages 1-26, January.
  21. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2022. "Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem," Energies, MDPI, vol. 15(13), pages 1-26, June.
  22. Marcin Blachnik & Sławomir Walkowiak & Adam Kula, 2023. "Large Scale, Mid Term Wind Farms Power Generation Prediction," Energies, MDPI, vol. 16(5), pages 1-15, March.
  23. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
  24. Paweł Ligęza, 2022. "Dynamic Error Correction Method in Tachometric Anemometers for Measurements of Wind Energy," Energies, MDPI, vol. 15(11), pages 1-9, June.
  25. Bogdan Bochenek & Jakub Jurasz & Adam Jaczewski & Gabriel Stachura & Piotr Sekuła & Tomasz Strzyżewski & Marcin Wdowikowski & Mariusz Figurski, 2021. "Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction," Energies, MDPI, vol. 14(8), pages 1-18, April.
  26. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
  27. Oliveira Santos, Victor & Costa Rocha, Paulo Alexandre & Scott, John & Van Griensven Thé, Jesse & Gharabaghi, Bahram, 2023. "Spatiotemporal analysis of bidimensional wind speed forecasting: Development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database," Energy, Elsevier, vol. 278(PA).
  28. Herbert Amezquita & Pedro M. S. Carvalho & Hugo Morais, 2023. "Wind Forecast at Medium Voltage Distribution Networks," Energies, MDPI, vol. 16(6), pages 1-23, March.
  29. Shahram Hanifi & Saeid Lotfian & Hossein Zare-Behtash & Andrea Cammarano, 2022. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models," Energies, MDPI, vol. 15(19), pages 1-21, September.
  30. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Aditya Sundararajan & Mohammed Olama & Yang Chen, 2023. "Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation," Energies, MDPI, vol. 16(7), pages 1-27, March.
  31. K. Ramakrishna Kini & Fouzi Harrou & Muddu Madakyaru & Ying Sun, 2023. "Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis," Energies, MDPI, vol. 16(15), pages 1-25, August.
  32. Minan Tang & Wenjuan Wang & Jiandong Qiu & Detao Li & Linyuan Lei, 2022. "Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales," Energies, MDPI, vol. 15(24), pages 1-21, December.
  33. Lee, Keunmin & Park, Bongjoon & Kim, Jeongwon & Hong, Jinkyu, 2024. "Day-ahead wind power forecasting based on feature extraction integrating vertical layer wind characteristics in complex terrain," Energy, Elsevier, vol. 288(C).
  34. Yuji Yamada & Takuji Matsumoto, 2023. "Construction of Mixed Derivatives Strategy for Wind Power Producers," Energies, MDPI, vol. 16(9), pages 1-26, April.
  35. Wen-Ko Hsu & Chung-Kee Yeh, 2021. "Offshore Wind Potential of West Central Taiwan: A Case Study," Energies, MDPI, vol. 14(12), pages 1-20, June.
  36. Kirchner-Bossi, Nicolas & Kathari, Gabriel & Porté-Agel, Fernando, 2024. "A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space," Applied Energy, Elsevier, vol. 367(C).
  37. Paweł Ligęza, 2021. "Basic, Advanced, and Sophisticated Approaches to the Current and Forecast Challenges of Wind Energy," Energies, MDPI, vol. 14(23), pages 1-10, December.
  38. Abdulrahman A. Alghamdi & Abdelhameed Ibrahim & El-Sayed M. El-Kenawy & Abdelaziz A. Abdelhamid, 2023. "Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-30, January.
  39. Christy Pérez-Albornoz & Ángel Hernández-Gómez & Victor Ramirez & Damien Guilbert, 2023. "Forecast Optimization of Wind Speed in the North Coast of the Yucatan Peninsula, Using the Single and Double Exponential Method," Clean Technol., MDPI, vol. 5(2), pages 1-22, June.
  40. Baggio, Roberta & Muzy, Jean-François, 2024. "Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration," Applied Energy, Elsevier, vol. 360(C).
  41. Meftah Elsaraiti & Adel Merabet, 2021. "A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed," Energies, MDPI, vol. 14(20), pages 1-16, October.
  42. Ju-Yeol Ryu & Bora Lee & Sungho Park & Seonghyeon Hwang & Hyemin Park & Changhyeong Lee & Dohyeon Kwon, 2022. "Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models," Energies, MDPI, vol. 15(24), pages 1-14, December.
  43. Hanifi, Shahram & Cammarono, Andrea & Zare-Behtash, Hossein, 2024. "Advanced hyperparameter optimization of deep learning models for wind power prediction," Renewable Energy, Elsevier, vol. 221(C).
  44. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
  45. Yakoub, Ghali & Mathew, Sathyajith & Leal, Joao, 2023. "Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models," Energy, Elsevier, vol. 263(PD).
  46. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
  47. Mahsa Dehghan Manshadi & Majid Ghassemi & Seyed Milad Mousavi & Amir H. Mosavi & Levente Kovacs, 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory," Energies, MDPI, vol. 14(16), pages 1-17, August.
  48. Hesong Cui & Xueping Li & Gongping Wu & Yawei Song & Xiao Liu & Derong Luo, 2021. "MPC Based Coordinated Active and Reactive Power Control Strategy of DFIG Wind Farm with Distributed ESSs," Energies, MDPI, vol. 14(13), pages 1-19, June.
  49. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.
  50. Takuji Matsumoto & Yuji Yamada, 2023. "Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power," Energies, MDPI, vol. 16(7), pages 1-22, March.
  51. Baïle, Rachel & Muzy, Jean-François, 2023. "Leveraging data from nearby stations to improve short-term wind speed forecasts," Energy, Elsevier, vol. 263(PA).
  52. Mikkel L. Sørensen & Peter Nystrup & Mathias B. Bjerregård & Jan K. Møller & Peder Bacher & Henrik Madsen, 2023. "Recent developments in multivariate wind and solar power forecasting," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
  53. Kübra Tümay Ateş, 2023. "Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
  54. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
  55. Guo, Nai-Zhi & Shi, Ke-Zhong & Li, Bo & Qi, Liang-Wen & Wu, Hong-Hui & Zhang, Zi-Liang & Xu, Jian-Zhong, 2022. "A physics-inspired neural network model for short-term wind power prediction considering wake effects," Energy, Elsevier, vol. 261(PA).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.