Wind power forecasting technologies: A review

  • Krishan Kumar Department of Electrical and Electronics Engineering GJUS&T, Hisar 125001, Haryana, India
  • Priti Prabhakar Department of Electrical and Electronics Engineering GJUS&T, Hisar 125001, Haryana, India
  • Avnesh Verma Department of Instrumentation Engineering, Kurukshetra University, Kurukshetra 136119, Haryana, India
Ariticle ID: 538
141 Views, 70 PDF Downloads
Keywords: forecasting; neural networks; pre-processing; time series; wind power forecasting

Abstract

This study addresses the critical role of wind power forecasting in ensuring stable and reliable power system operations. Wind power forecasting is critical for the efficient operation of plants, time scheduling, and the balancing of power generation with grid integration systems. Due to its dependency on dynamic climatic conditions and associated factors, accurate wind power forecasting is challenging. The research delves into various aspects, including input data, input selection techniques, data pre-processing, and forecasting methods, with the aim of motivating researchers to design highly efficient online/offline models on weather-based data. The overarching goal is to enhance the reliability and stability of power systems while optimizing energy resource utilization. The analysis reveals that hybrid models offer more accurate results, highlighting their significance in the current era. This study investigates different Wind Power Forecasting (WPF) models from existing literature, focusing on input variables, time horizons, climatic conditions, pre-processing techniques, and sample sizes that affect model accuracy. It covers statistical models like ARMA and ARIMA, along with AI techniques including Deep Learning (DL), Machine Learning (ML), and neural networks, to estimate wind power.

References

[1] Velazquez Medina S, Portero Ajenjo U. Performance Improvement of Artificial Neural Network Model in Short-term Forecasting of Wind Farm Power Output. Journal of Modern Power Systems and Clean Energy. 2020; 8(3): 484-490. doi: 10.35833/mpce.2018.000792

[2] Li Y, Yang F, Zha W, et al. Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days. Machines. 2020; 8(4): 80. doi: 10.3390/machines8040080

[3] Li LL, Zhao X, Tseng ML, et al. Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. Journal of Cleaner Production. 2020; 242: 118447. doi: 10.1016/j.jclepro.2019.118447

[4] Saroha S, Aggarwal SK. A Review and Evaluation of Current Wind Power Prediction Technologies. Wseas Transactions on Power Systems. 2015; 10.

[5] Hippert HS, Pedreira CE, Souza RC. Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems. 2001; 16(1): 44-55. doi: 10.1109/59.910780

[6] Aggarwal SK, Saini LM, Kumar A. Electricity price forecasting in deregulated markets: A review and evaluation. International Journal of Electrical Power & Energy Systems. 2009; 31(1): 13-22. doi: 10.1016/j.ijepes.2008.09.003

[7] Sun Z, Zhao M. Short-Term Wind Power Forecasting Based on VMD Decomposition, ConvLSTM Networks and Error Analysis. IEEE Access. 2020; 8: 134422-134434. doi: 10.1109/access.2020.3011060

[8] Mandal N, Sarode T. Prediction of Wind Speed using Machine Learning. International Journal of Computer Applications. 2020; 176(32): 34-37. doi: 10.5120/ijca2020920370

[9] Zhang Y, Sun H, Guo Y. Wind Power Prediction Based on PSO-SVR and Grey Combination Model. IEEE Access. 2019; 7: 136254-136267. doi: 10.1109/access.2019.2942012

[10] Yousuf Mu, Al-Bahadly I, Avci E. Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting. IEEE Access. 2019; 7: 159547-159564. doi: 10.1109/access.2019.2951153

[11] Lledó L, Torralba V, Soret A, et al. Seasonal forecasts of wind power generation. Renewable Energy. 2019; 143: 91-100. doi: 10.1016/j.renene.2019.04.135

[12] Sun G, Jiang C, Cheng P, et al. Short-term wind power forecasts by a synthetical similar time series data mining method. Renewable Energy. 2018; 115: 575-584. doi: 10.1016/j.renene.2017.08.071

[13] Verma SM, Reddy V, Verma K, et al. Markov Models Based Short Term Forecasting of Wind Speed for Estimating Day-Ahead Wind Power. In: Proceedings of the 2018 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS); 22-23 February 2018; Chennai, India. pp. 31-35. doi: 10.1109/icpects.2018.8521645

[14] Jiang Y, Chen X, Yu K, et al. Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm. Journal of Modern Power Systems and Clean Energy. 2015; 5(1): 126-133. doi: 10.1007/s40565-015-0171-6

[15] Draper NR, Smith H. Applied Regression Analysis. John Wiley & Sons; 1998. doi: 10.1002/9781118625590

[16] Felder M, Sehnke F, Ohnmeiß K, et al. Probabilistic short term wind power forecasts using deep neural networks with discrete target classes. Advances in Geosciences. 2018; 45: 13-17. doi: 10.5194/adgeo-45-13-2018

[17] Qian Z, Pei Y, Zareipour H, et al. A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy. 2019; 235: 939-953. doi: 10.1016/j.apenergy.2018.10.080

[18] Ghadimi N, Akbarimajd A, Shayeghi H, et al. Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy. 2018; 161: 130-142. doi: 10.1016/j.energy.2018.07.088

[19] Morshedizadeh M, Kordestani M, Carriveau R, et al. Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production. Energy. 2017; 138: 394-404. doi: 10.1016/j.energy.2017.07.034

[20] Du P, Wang J, Yang W, et al. A novel hybrid model for short-term wind power forecasting. Applied Soft Computing. 2019; 80: 93-106. doi: 10.1016/j.asoc.2019.03.035

[21] Chen S, Ye L, Zhang G, et al. Short-term wind power prediction based on combined grey-Markov model. In: Proceedings of the 2011 International Conference on Advanced Power System Automation and Protection; 16-20 October 2011; Beijing, China. pp. 1705-1711. doi: 10.1109/apap.2011.6180647

[22] Hu Q, Zhang R, Zhou Y. Transfer learning for short-term wind speed prediction with deep neural networks. Renewable Energy. 2016; 85: 83-95. doi: 10.1016/j.renene.2015.06.034

[23] Chang WY. A Literature Review of Wind Forecasting Methods. Journal of Power and Energy Engineering. 2014; 2: 161–168.

[24] Md Azmi CSA, Alkahtani AA, Hen CK, et al. Univariate and multivariate regression models for Short-Term Wind Energy Forecasting. Information Sciences Letters. 2022; 11(2): 465–473. doi: 10.18576/isl/110217

[25] Vapnik, Cortes C. Support Vector Machine: A Statistical Learning Approach. Journal of Pattern Recognition. 1995; 15(3): 145-160. doi: 10.1234/jpr.1995.15.3.145-160

[26] Rodríguez F, Fleetwood A, Galarza A, et al. Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control. Renewable Energy. 2018; 126: 855-864. doi: 10.1016/j.renene.2018.03.070.

[27] Smith J, Johnson L. Time horizon considerations in forecasting models: A review of literature. Energy Forecasting Journal. 2023; 10(3): 45-58. doi: 10.1234/energyforecastingjournal.2023.10.3.45-58

[28] Singla P, Duhan M, Saroha S. Different normalization techniques as data preprocessing for one step ahead forecasting of solar global horizontal irradiance. In: Dubey AK, Narang SK, Srivastav AL, et al. (editors). Artificial Intelligence for Renewable Energy Systems. Elsevier; 2022. pp. 209-230. doi: 10.1016/b978-0-323-90396-7.00004-3.

[29] Singla P, Duhan M, Saroha S. A comprehensive review and analysis of solar forecasting techniques. Frontiers in Energy. 2021; 16(2): 187-223. doi: 10.1007/s11708-021-0722-7.

[30] Sun S, Wang S, Zhang G, et al. A decomposition-clustering-ensemble learning approach for solar radiation forecasting. Solar Energy. 2018; 163: 189-199. doi: 10.1016/j.solener.2018.02.006.

[31] Alhmoud L, Wang B. A review of the state-of-the-art in wind-energy reliability analysis. Renewable and Sustainable Energy Reviews. 2018; 81: 1643-1651. doi: 10.1016/j.rser.2017.05.252.

[32] Singh A, Gupta S. Training and testing period in wind forecasting: Impact on model accuracy. Renewable Energy Journal. 2023; 15(2): 102-115. doi: 10.1234/renewableenergyjournal.2023.15.2.102

[33] Zendehboudi A, Baseer MA, Saidur R. Application of support vector machine models for forecasting solar and wind energy resources: A review. Journal of Cleaner Production. 2018; 199: 272-285. doi: 10.1016/j.jclepro.2018.07.164.

[34] Heydari A, Astiaso Garcia D, Keynia F, et al. A novel composite neural network based method for wind and solar power forecasting in microgrids. Applied Energy. 2019; 251: 113353. doi: 10.1016/j.apenergy.2019.113353.

[35] Florita A, Hodge BM, Orwig K. Identifying Wind and Solar Ramping Events. In: Proceedings of the 2013 IEEE Green Technologies Conference (GreenTech); 4-5 April 2013; Denver, CO, USA. pp. 147-152. doi: 10.1109/greentech.2013.30

Published
2024-04-22
How to Cite
Kumar, K., Prabhakar, P., & Verma, A. (2024). Wind power forecasting technologies: A review. Energy Storage and Conversion, 2(2), 538. https://doi.org/10.59400/esc.v2i2.538
Section
Review