Review and Assessment of Wind Power Forecasting Studies for Very Short-Term and Short-Term Horizons

Abstract

Wind energy penetration is continuously increasing in electricity grids and the intermittent nature of wind speed causes the problems in system operations. Therefore, utilities, system operators and researchers focus on alleviating the negative impacts of volatile generation and harvesting wind energy efficiently. At this point, accurate wind power forecasts serve as the promising research studies in the literature. To this end, this paper presents a comprehensive literature review of wind power forecasting studies for very short-term and short-term horizons. The reviewed studies have been compared in terms of installation properties of wind power plants, inputs of forecast models, data recording intervals and periods, training, validation and test subsets, forecast horizons, accuracy measures and forecast performance. As a result of the knowledge-intensive literature tables created, the up-to-date assessments of very short-term and short-term forecasting studies have been made from different perspectives, and noteworthy recommendations have been highlighted for fairer comparisons of the reviewed studies.

Keywords

• Wind energy
• wind power
• forecast horizon
• review
• assessment

Çok Kısa Dönem ve Kısa Dönem Ufukları için Rüzgâr Gücü Tahmin Çalışmalarının İncelenmesi ve Değerlendirilmesi

Öz

Elektrik şebekelerinde rüzgâr enerjisinin penetrasyonu sürekli olarak artmakta ve rüzgâr hızının kesintili doğası sistem operasyonlarında sorunlara neden olmaktadır. Bu nedenle, kamu hizmet kuruluşları, sistem operatörleri ve araştırmacılar değişken üretimin olumsuz etkilerini hafifletmeye ve rüzgâr enerjisini verimli bir şekilde toplamaya odaklanmaktadır. Bu noktada, doğru rüzgâr gücü tahminleri literatürde umut vadeden araştırma çalışmaları olarak hizmet vermektedir. Bu amaçla, bu makale çok kısa dönem ve kısa dönem ufukları için rüzgâr gücü tahmin çalışmalarının kapsamlı bir literatür incelemesini sunmaktadır. İncelenen çalışmalar rüzgâr güç santrallerinin kurulum özellikleri, tahmin modellerinin girdileri, veri kayıt aralıkları ve periyotları, eğitim, doğrulama ve test alt kümeleri, tahmin ufukları, doğruluk ölçümleri ve tahmin performansı açısından karşılaştırılmıştır. Oluşturulan bilgi yoğun literatür tabloları sonucunda, çok kısa dönem ve kısa dönem tahmin çalışmalarının güncel değerlendirmeleri farklı perspektiflerden yapılmış ve incelenen çalışmaların daha adil karşılaştırmaları için dikkate değer öneriler vurgulanmıştır.

Anahtar Kelimeler

• Rüzgâr enerjisi
• rüzgâr gücü
• tahmin ufku
• inceleme
• değerlendirme

Kaynakça

1. [1] Majeed Y, Khan MU, Waseem M, Zahid U, Mahmood F, Majeed F, Sultan M, Raza A. Renewable energy as an alternative source for energy management in agriculture. Energy Reports. 2023; 10: 344-359.
2. [2] Yesilbudak M. Implementation of novel hybrid approaches for power curve modeling of wind turbines. Energy Conversion and Management. 2018; 171: 156-169.
3. [3] Ang TZ, Salem M, Kamarol M, Das HS, Nazari MA, Prabaharan N. A comprehensive study of renewable energy sources: Classifications, challenges and suggestions. Energy Strategy Reviews. 2022; 43: 100939.
4. [4] Celik I, Yildiz C, Sekkeli M. An optimization model for wind turbine micro-siting in wind power plant installation. Gazi University Journal of Science Part C: Design and Technology. 2018; 6(4): 898-908.
5. [5] Yesilbudak M, Ozcan A. kNN classifier applications in wind and solar energy systems. 11th International Conference on Renewable Energy Research and Applications. 2022; 480-484.
6. [6] Chen C, Hu Y, Karuppiah M, Kumar PM. Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustainable Energy Technologies and Assessments. 2021; 47: 101358.
7. [7] Yesilbudak M, Ozcan A. Wind turbine power curve fitting using mountain gazelle optimizer and parametric functions. 12th International Conference on Smart Grid. 2024; 772-776.
8. [8] Oflaz AE, Yesilbudak M. Comparison of outlier detection approaches for wind turbine power curves. 10th International Conference on Smart Grid. 2022; 363-367.

9. [9] Bassey KE, Juliet AR, Stephen AO. AI-Enhanced lifecycle assessment of renewable energy systems. Engineering Science & Technology Journal. 2024; 5(7): 2082-2099.
10. [10] Abisoye BO, Sun Y, Zenghui W. A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights. Renewable Energy Focus. 2024; 48: 100529.
11. [11] Sadorsky P. Wind energy for sustainable development: Driving factors and future outlook. Journal of Cleaner Production. 2021; 289: 125799.
12. [12] Yesilbudak M, Sagiroglu S, Colak I. A new approach to very short term wind speed prediction using k-nearest neighbour classification. Energy Conversion and Management. 2013; 69: 77-86.
13. [13] Yin L, Zhao M. Inception-embedded attention memory fully-connected network for short-term wind power prediction. Applied Soft Computing. 2023; 141: 110279.
14. [14] Chen Y, Yu S, Islam S, Lim CP, Muyeen SM. Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions. Energy Reports. 2022; 8: 8805-8820.
15. [15] Internet: Global Wind Report 2024. Available at: https://gwec.net/global-wind-report-2024 (Last accessed on November 1, 2024)
16. [16] Yesilbudak M, Sagiroglu S, Colak I. A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction. Energy Conversion and Management. 2017; 135: 434-444.
17. [17] Yan H, Wu Z. A hybrid short-term wind power prediction model combining data processing, multiple parameters optimization and multi-intelligent models apportion strategy. IEEE Access. 2020; 8: 227126-227140.
18. [18] Yan J, Mohrlen C, Gocmen T, Kelly M, Wessel A, Giebel G. Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain. Renewable and Sustainable Energy Reviews. 2022; 165: 112519.
19. [19] Yesilbudak M, Ozcan A. Performance comparison of metaheuristic optimization-based parametric methods in wind turbine power curve modeling. IEEE Access. 2024; 12: 99372-99381.
20. [20] Yesilbudak M. A novel power curve modeling framework for wind turbines. Advances in Electrical and Computer Engineering. 2019; 19(3): 29-40.
21. [21] Wang C, Zhang H, Fan W, Fan X. A new wind power prediction method based on chaotic theory and Bernstein neural network. Energy. 2016; 117: 259-271.
22. [22] Alkhayat G, Mehmood R. A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy and AI. 2021; 4: 100060.
23. [23] Tuncar EA, Saglam S, Oral B. A review of short-term wind power generation forecasting methods in recent technological trends. Energy Reports. 2024; 12: 197-209.
24. [24] Wang Y, Wang D, Tang Y. Clustered hybrid wind power prediction model based on ARMA, PSO-SVM, and clustering methods. IEEE Access. 2020; 8: 17071-17079.
25. [25] Eyecioglu O, Hangun B, Kayisli K, Yesilbudak M. Performance comparison of different machine learning algorithms on the prediction of wind turbine power generation. 8th International Conference on Renewable Energy Research and Applications. 2019; 922-926.
26. [26] Chen Y, Hua X, Zhang L. A review of ultra-short-term forecasting of wind power based on data decomposition - forecasting technology combination model. Energy Reports. 2022; 8: 14200-14219.
27. [27] Zhao E, Sun S, Wang S. New developments in wind energy forecasting with artificial intelligence and big data: A scientometric insight. Data Science and Management. 2022; 5(2): 84-95.
28. [28] Dai X, Liu GP, Hu W. An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting. Energy. 2023; 272: 127173.
29. [29] Guobin F, Xuebin W, Han Z, Rui S, Yang X, Dingran C, Yang C, Renyue J. The VMD-CNN-Trans model used for ultra-short-term power prediction in wind farms. IEEE Sustainable Power and Energy Conference. 2023; 1-5.
30. [30] Van Heerden L, Vermeulen HJ, Van Staden C. Wind power forecasting using hybrid recurrent neural networks with empirical mode decomposition. IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems. 2022; 1-6.
31. [31] Zhang J, Li H, Cheng P, Yan J. Interpretable wind power short-term power prediction model using deep graph attention network. Energies. 2024; 17(2): 384.
32. [32] Xiao Y, Zou C, Chi H, Fang R. Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis. Energy. 2023; 267: 126503.
33. [33] Sun Z, Zhao M. Short-term wind power forecasting based on VMD decomposition, ConvLSTM networks and error analysis. IEEE Access. 2020; 8: 134422-134434.
34. [34] Sun Z, Zhao S, Zhang J. Short-term wind power forecasting on multiple scales using VMD decomposition, k-means clustering and LSTM principal computing. IEEE Access. 2019; 7: 166917-166929.
35. [35] Karijadi I, Chou SY, Dewabharata A. Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method. Renewable Energy. 2023; 218: 119357.
36. [36] Li Y, Wu Z, Su Y. Adaptive short-term wind power forecasting with concept drifts. Renewable Energy. 2023; 217: 119146.
37. [37] Wang S, Li B, Li G, Yao B, Wu J. Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration. Applied Energy. 2021; 292: 116851.
38. [38] Ma J, Wang C, Yang M, Lin Y. Ultra-short-term probabilistic wind turbine power forecast based on empirical dynamic modeling. IEEE Transactions on Sustainable Energy. 2020; 11(2): 906-915.
39. [39] Li L, Cen ZY, Tseng ML, Shen Q, Ali MH. Improving short-term wind power prediction using hybrid improved cuckoo search arithmetic-support vector regression machine. Journal of Cleaner Production. 2021; 279: 123739.
40. [40] Qian Z, Wen S, Zhang L, Zhang J, Yuan S, Mao L, Zhou L. Deep learning-based short-term wind power prediction considering various factors. 17th International Conference on Control, Automation, Robotics and Vision. 2022; 529-533.
41. [41] Colak I, Sagiroglu S, Yesilbudak M, Kabalci E, Bulbul HI. Multi-time series and -time scale modeling for wind speed and wind power forecasting Part I: Statistical methods, very short-term and short-term applications. International Conference on Renewable Energy Research and Applications. 2015; 209-214.
42. [42] An G, Chen L, Tan J, Jiang Z, Li Z, Sun H. Ultra-short-term wind power prediction based on PVMD-ESMA-DELM. Energy Reports. 2022; 8: 8574-8588.
43. [43] Wang J, Tang X. An intensive decomposition integration paradigm for short-term wind power forecasting based on feature extraction and optimal weighted combination strategy. Measurement. 2023; 223: 113811.
44. [44] Li N, Dong J, Liu L, Li H, Yan J. A novel EMD and causal convolutional network integrated with transformer for ultra-short-term wind power forecasting. International Journal of Electrical Power and Energy Systems. 2023; 154: 109470.
45. [45] An G, Jiang Z, Liang Y, Zhao Y, Li Z, Dong W, Sun H. Short-term wind power prediction based on particle swarm optimization-extreme learning machine model combined with Adaboost algorithm. IEEE Access. 2021; 9: 94040-94052.
46. [46] Hu T, Wu W, Guo Q, Sun H, Shi L, Shen X. Very short-term spatial and temporal wind power forecasting: A deep learning approach. CSEE Journal of Power and Energy Systems. 2020; 6(2): 434-443.
47. [47] Xiang L, Liu J, Yang X, Hu A, Su H. Ultra-short term wind power prediction applying a novel model named SATCN-LSTM. Energy Conversion and Management. 2022; 252: 115036.
48. [48] Aslam M, Kim JS, Jung J. Multi-step ahead wind power forecasting based on dual-attention mechanism. Energy Reports. 2023; 9: 239-251.
49. [49] Huang J, Zhang W, Qin J, Song S. Ultra-short-term wind power prediction based on eEEMD-LSTM. Energies. 2024; 17(1): 251.
50. [50] Du P, Wang J, Yang W, Niu T. A novel hybrid model for short-term wind power forecasting. Applied Soft Computing. 2019; 80: 93-106.
51. [51] Wang J, Zhu H, Zhang Y, Cheng F, Zhou C. A novel prediction model for wind power based on improved long short-term memory neural network. Energy. 2023; 265: 126283.
52. [52] Tan L, Han J, Zhang H. Ultra-short-term wind power prediction by salp swarm algorithm-based optimizing extreme learning machine. IEEE Access. 2020; 8: 44470-44484.
53. [53] Zhang G, Liu H, Zhang J, Yan Y, Zhang L, Wu C, Hua X, Wang Y. Wind power prediction based on variational mode decomposition multi-frequency combinations. Journal of Modern Power Systems and Clean Energy. 2019; 7(2): 281-288.
54. [54] Chen G, Shan J, Li DY, Wang C, Li C, Zhou Z, Wang X, Li Z, Hao JJ. Research on wind power prediction method based on convolutional neural network and genetic algorithm. IEEE Innovative Smart Grid Technologies-Asia. 2019; 3573-3578.
55. [55] Zhang H, Zhu Q, Li W, Li X, Xie C, Xiang C, Fan J. Ultra short term power forecasting of wind generation based on improved LSTM. 3rd Power System and Green Energy Conference. 2023; 176-180.
56. [56] Xiong B, Meng X, Xiong G, Ma H, Lou L, Wang Z. Multi-branch wind power prediction based on optimized variational mode decomposition. Energy Reports. 2022; 8: 11181-11191.
57. [57] Li J, Ma L. Short-term wind power prediction based on soft margin multiple kernel learning method. Chinese Journal of Electrical Engineering. 2022; 8(1): 70-80.
58. [58] Liu ZH, Wang CT, Wei HL, Zeng B, Li M, Song XP. A wavelet-LSTM model for short-term wind power forecasting using wind farm Scada data. Expert Systems with Applications. 2024; 247: 123237.
59. [59] Abdulkadir AK, Chen Y, Wang YW, Xiao JW. Hybrid model based on temporal convolutional network and attention mechanism for short-term wind power forecasting. IEEE 7th International Electrical and Energy Conference. 2024; 149-154.
60. [60] Sun S, Du Z, Jin K, Li H, Wang S. Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy. Applied Energy. 2023; 350: 121749.
61. [61] Fu X, Gao F, Wu J, Wei X, Duan F. Spatiotemporal attention networks for wind power forecasting. International Conference on Data Mining. 2019; 149-154.
62. [62] Li Y, Wang R, Li Y, Zhang M, Long C. Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach. Applied Energy. 2023; 329: 120291.
63. [63] Sheng Y, Wang H, Yan J, Liu Y, Han S. Short-term wind power prediction method based on deep clustering-improved temporal convolutional network. Energy Reports. 2023; 9: 2118-2129.
64. [64] Meka R, Alaeddini A, Bhaganagar K. A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables. Energy. 2021; 221: 119759.
65. [65] Wang Z, Ying Y, Kou L, Ke W, Wan J, Yu Z, Liu H, Zhang F. Ultra-short-term offshore wind power prediction based on PCA-SSA-VMD and BiLSTM. Sensors. 2024; 24(2): 444.
66. [66] Jiang T, Liu Y. A short-term wind power prediction approach based on ensemble empirical mode decomposition and improved long short-term memory. Computers and Electrical Engineering. 2023; 110: 108830.
67. [67] Zheng L, Xiaorui L, Mengjie L, Xin C, Shenhui D, Hexu S. Wind power prediction based on EEMD-Tent-SSA-LS-SVM. Energy Reports. 2022; 8: 3234-3243.
68. [68] Wang Y, Tong T, Hu Z, Wu K, Zhou H, Zheng Y. An ultra short-term wind power forecasting model based on meteorological feature selection. IEEE 7th International Electrical and Energy Conference. 2024; 1-6.
69. [69] Zhao L, Su R, Shen G, Qi W, Jiao X, Wang J. Ultra-short term wind power forecast for offshore energy system based on EEMD-TCN. 8th Asia Conference on Power and Electrical Engineering. 2023; 2491-2495.
70. [70] Yin H, Ou Z, Zhu Z, Xu X, Fan J, Meng A. A novel asexual-reproduction evolutionary neural network for wind power prediction based on generative adversarial networks. Energy Conversion and Management. 2021; 247: 114714.
71. [71] Ouyang T, Zha X, Qin L. A combined multivariate model for wind power prediction. Energy Conversion and Management. 2017; 144: 361-373.
72. [72] Ayele ST, Ageze MB, Zeleke MA, Miliket TA. Adama II wind farm long-term power generation forecasting based on machine learning models. Scientific African. 2023; 21: e01831.
73. [73] Han Y, Tong X. Multi-step short-term wind power prediction based on three-level decomposition and improved grey wolf optimization. IEEE Access. 2020; 8: 67124-67136.
74. [74] Miao C, Li H, Wang X, Li H. Ultra-short-term prediction of wind power based on sample similarity analysis. IEEE Access. 2021; 9: 72730-72742.
75. [75] Gu B, Hu H. Zhao J, Zhang H, Liu X. Short-term wind power forecasting and uncertainty analysis based on FCM-WOA-ELM-GMM. Energy Reports. 2023; 9: 807-819.
76. [76] Yang M, Chen X, Du J, Cui Y. Ultra-short-term multistep wind power prediction based on improved EMD and reconstruction method using run-length analysis. IEEE Access. 2018; 6: 31908-31917.
77. [77] Li LL, Liu ZF, Tseng ML, Jantarakolica K, Lim MK. Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power. Expert Systems with Applications. 2021; 184: 115579.
78. [78] Shobanadevi A, Maragatham G, Prabu MR, Boopathi K. Short-term wind power forecasting using R-LSTM. International Journal of Renewable Energy Research. 2021; 11(1): 392-406.
79. [79] Qin J, Yang J, Chen Y, Ye Q, Li H. Two‐stage short‐term wind power forecasting algorithm using different feature-learning models. Fundamental Research. 2021; 1(4): 472-481.
80. [80] Mo S, Wang H, Li B, Xue Z, Fan S, Liu X. Powerformer: A temporal-based transformer model for wind power forecasting. Energy Reports. 2024; 11: 736-744.
81. [81] Zhu R, Liao W, Wang Y. Short-term prediction for wind power based on temporal convolutional network. Energy Reports. 2020; 6: 424-429.
82. [82] Zameer A, Arshad J, Khan A, Raja MAZ. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Conversion and Management. 2017; 134: 361-372.
83. [83] Chen C, Liu H. Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection. Energy Conversion and Management. 2020; 206: 112492.
84. [84] Han L, Jing H, Zhang R, Gao Z. Wind power forecast based on improved long short term memory network. Energy. 2019; 189: 116300.
85. [85] Zhu J, Su L, Li Y. Wind power forecasting based on new hybrid model with TCN residual modification. Energy and AI. 2022; 10: 100199.
86. [86] Shahid F, Zameer A, Mehmood A, Raja MAZ. A novel wavenets long short term memory paradigm for wind power prediction. Applied Energy. 2020; 269: 115098.
87. [87] Shahid F, Zameer A, Muneeb M. A novel genetic LSTM model for wind power forecast. Energy. 2021; 223: 120069.
88. [88] Zhu N, Dai Z, Wang Y, Zhang K. A contrastive learning-based framework for wind power forecast. Expert Systems with Applications. 2023; 230: 120619.
89. [89] Yuzgec U, Dokur E, Balci M. A novel hybrid model based on empirical mode decomposition and echo state network for wind power forecasting. Energy. 2024; 300: 131546.
90. [90] Sheng A, Xie L, Zhou Y, Wang Z, Liu Y. A hybrid model based on complete ensemble empirical mode decomposition with adaptive noise, GRU network and whale optimization algorithm for wind power prediction. IEEE Access. 2023; 11: 62840-62854.
91. [91] Zheng H, Hu Z, Wang X, Ni J, Cui M. VMD-CAT: A hybrid model for short-term wind power prediction. Energy Reports. 2023; 9: 199-211.
92. [92] Ding Y, Chen Z, Zhang H, Wang X, Guo Y. A short-term wind power prediction model based on CEEMD and WOA-KELM. Renewable Energy. 2022; 189: 188-198.
93. [93] Hu S, Wang Z, Hu N. Short-term wind power prediction based on whale algorithm optimization and attention mechanism of BiLSTM neural network. 5th International Conference on Power, Intelligent Computing and Systems. 2023; 915-920.
94. [94] Gao Z, Gao Z, Cao S, Zhang L. A GSA-VMD-BiLSTM-MHSA model for short-term wind power prediction. 4th International Conference on Energy Engineering and Power Systems. 2024; 261-266.
95. [95] Tongkun S, Zhenhao T. 2-hour ahead wind power prediction based on multi-model fusion. International Conference on Power Science and Technology. 2023; 787-792.
96. [96] Ahmed U, Abbas SS, Razzaq S, Mahmood A. A novel integrated approach for short-term wind power forecasting. 7th International Conference on Energy Conservation and Efficiency. 2024; 1-6.
97. [97] Hu C, Zhang Z, Zhao P, Yi H. Short-term wind power forecasting with multiple train-test splits integrated temporal convolutional network. IEEE/IAS Industrial and Commercial Power System Asia. 2022; 1234-1238.
98. [98] Liu L, Ji TY, Li MS, Wu QH. Short-term wind power local forecast based on morphological high-frequency filter and local-sensitive hashing. IEEE PES GTD Grand International Conference and Exposition Asia. 2019; 685-690.
99. [99] Tarek Z, Shams M., Elshewey AM, El-kenawy ESM, Ibrahim A, Abdelhamid AA, El-dosuky MA. Wind power prediction based on machine learning and deep learning models. Computers, Materials & Continua. 2023; 75(1): 715-732.
100. [100] Yuan X, Chen C, Yuan Y, Huang Y, Tan Q. Short-term wind power prediction based on LSSVM-GSA model. Energy Conversion and Management. 2015; 101: 393-401.
101. [101] Xing Z, He Y. Multi-modal multi-step wind power forecasting based on stacking deep learning model. Renewable Energy. 2023; 215: 118991.