Wind power forecasting technologies: A review

Krishan Kumar; Priti Prabhakar; Avnesh Verma

doi:10.59400/esc.v2i3.538

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

314 Views, 155 PDF Downloads

DOI: https://doi.org/10.59400/esc.v2i3.538

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

2024-08-22

How to Cite

Kumar, K., Prabhakar, P., & Verma, A. (2024). Wind power forecasting technologies: A review. Energy Storage and Conversion, 2(3), 538. https://doi.org/10.59400/esc.v2i3.538

Download Citation

Issue

Vol. 2 No. 3 (2024)

Section

Review

This work is licensed under a Creative Commons Attribution 4.0 International License.

Editor-in-Chief

Prof. Xiaohu Yang

Xi'an Jiaotong University, China

eISSN

3029-2778

Publication Frequency

Quarterly

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2024

2023

Featured Articles

Nitrided Copper-Iron Composite Oxides Derived from Layered Double Hydroxides for Enhanced Carbon Dioxide Electroreduction to Methane and Formic Acid

The reduction of carbon dioxide to valuable chemical products is a promising solution to address carbon balance and energy issues. Herein, amorphous nitrided copper-iron oxides are prepared by gas-phase nitriding of Cu Fe-layered double hydroxide precursor s with urea as nitrogen source. Amorphous materials are more likely to generate defect vacancies during the reaction process, and these vacancies can function as active sites for catalytic reactions. Therefore, the obtained materials show high activity for CO 2 electroreduction to methane and formic acid, achieving a total Faraday efficiency of 74.7% at −0.7 V vs RHE and exhibiting a continuous 10 h durability in the H-cell. The uniformly distributed Cu + sites act as active sites by losing electrons to activate CO 2 . During the CO 2 electroreduction , CO 2 is converted to *COOH via proton-electron coupling, *COOH combines directly with a proton in solution to produce the HCOOH product, and the other part of *COOH undergoes a protonated dehydration process to form the *CHO intermediate which dehydrates again to form CH 4 . This study provides a new approach for designing CO 2 electroreduction catalysts.

AlZnO magnetron sputtered thin film for photovoltaic application

Aluminum zinc oxide (AZO) is a nontoxic and a low-cost material that finds application as a transparent conducting electrode in photovoltaic devices. In this study the (direct current) DC magnetron sputtering of AZO films is carried out at different deposition times of 5, 10, 15, 20 and 25 min’s at room temperature and it’s structural, optical, electrical and morphological properties are studied for its use as a front contact for thin film solar cell application. The structural study suggests that the preferred orientation of grains along (002) plane having hexagonal structure and the optical and the electrical studies suggest that the films show an average transmission of 70% and a resistivity of the order of 10 -4 Ωcm. On the other hand, the scanning electron microscopy (SEM) images suggest the formation of packed grains having a homogeneous surface. Moreover in order to study the optoelectronic properties of prepared samples, the electronic and optical calculations of the AZO are performed by the first-principles calculations using density functional theory (DFT).

Investigation of self-excited induction generator for supporting domestic loads and its extension to a microgrid

This study provides a technical and financial analysis for the incorporation of a microgrid structure with a wind energy conversion system for producing electricity. This study’s primary aim is to provide solutions for issues that arise when isolated induction generators are employed with microgrids. A closed-loop smart electronic load controller is used to regulate the loads in the system that are supplied by the generation. A switched variable capacitor bank is used to supply reactive power initially during a voltage dip at varying winds and loads to sustain the voltage profile. Additionally, a simple voltage control loop-based controller for the generator side converter maintains the voltage at a steady level. Using HOMER software, an economic study of the suggested wind-based microgrid structure is also presented. A laboratory experimental setup is used to support the MATLAB/Simulink study of the proposed method and its control. The findings back the feasibility of implementing the suggested plan in grid-isolated regions for supplying critical loads.

Research progress on ZnO/MoS2/rGO ternary photocatalysts

Energy shortage and environmental pollution have become one of the important global issues, and semiconductor photocatalytic technology is considered as one of the effective means to solve these problems. As a new and efficient green material, ZnO has attracted wide attention. ZnO is widely used in the field of photocatalysis due to its non-toxicity, low cost, environmental friendliness, adjustable band gap, high electron density, and chemical stability. However, the recombination of photogenerated charge carriers in ZnO hinders its practical application and lowers the utilization efficiency of visible light. On the other hand, molybdenum disulfide/reduced graphene oxide (MoS 2 /rGO), as a binary non-precious metal co-catalyst, has a larger specific surface area, suitable band gap width, and visible light response capability compared to single-phase graphene co-catalyst. Therefore, introducing MoS 2 /rGO co-catalyst into the ZnO system can provide more active sites, reduce the probability of photogenerated charge carrier recombination, and improve the utilization efficiency of visible light. In this review, we summarize the hydrothermal synthesis methods for preparing this highly demanded nanocomposite material, including one-step and stepwise methods. Subsequently, we elaborate on the mechanism of enhancing light absorption and achieving efficient electron-hole separation behavior in the ternary system heterojunction structure during the photocatalytic process. Due to its significant advantages, this ternary system heterojunction structure has been widely applied in the field of photocatalysis, including applications such as pollutant degradation, sterilization, and water splitting.

Wind power forecasting technologies: A review

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 plant, time scheduling, and it’s 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.