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A structure for predicting wind speed using fuzzy granulation and optimization techniques

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Abstract

With the increasing scarcity of global energy, the rapid development of science and technology, and the growing demand for environmental protection, wind energy is receiving increasing attention as the cleanest source of energy. Due to its pollution-free nature and widespread availability, it has become a preferred source of electricity generation in many countries. However, wind speed prediction plays a vital role in wind power generation. Traditional prediction models, due to randomness and uncertainty, often produce unstable and inaccurate results, leading to power and economic losses. Therefore, this study proposes a hybrid prediction system based on an information processing strategy and a multi-objective optimization algorithm. By preprocessing the data and optimizing the combination of five individual models, the singularity of a single model is overcome, a Pareto-optimal solution is obtained, and accurate and stable prediction results are provided. To verify the effectiveness of the proposed combined model in predicting wind speed, various experiments on a wind speed series were conducted based on a wind power station located in Penglai, China. The results show that the combined model proposed in this study has better prediction performance than conventional models.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. World Wind Energy Association. (2018). Wind Power Capacity reaches 539 GW, 52, 6 GW added in 2017. WWEA, Bonn. Online https://wwindea.org/2017-statistics/. Accessed 28 May 2022

  2. Council GWE (2017) GWEC Global Wind Report 2019. Global Wind Energy Council: Bonn, Germany. https://gwec.net/global-wind-report-2019/. Accessed 28 May 2022

  3. Zhou Y, Wang J, Lu H et al (2022) Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition[J]. Chaos Solit Fractals 157:111. https://doi.org/10.1016/j.chaos.2022.111982

    Article  MathSciNet  Google Scholar 

  4. Lu KD, Wu ZG, Huang T (2022) Differential evolution-based three stage dynamic cyber-attack of cyber-physical power systems. IEEE/ASME Trans Mechatron. https://doi.org/10.1109/TMECH.2022.3214314

    Article  Google Scholar 

  5. Tian C, Hao Y, Hu J (2018) A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization. Appl Energy 231:301–319. https://doi.org/10.1016/j.apenergy.2018.09.012

    Article  Google Scholar 

  6. Wang L, Li J (2016) Estimation of extreme wind speed in SCS and NWP by a non-stationary model. Theoretical & Applied Mechanics Letters 6(03):131–138. https://doi.org/10.1016/j.taml.2016.04.001

    Article  Google Scholar 

  7. Cassola F, Burlando M (2012) Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output. Appl Energy 99:154–166. https://doi.org/10.1016/j.apenergy.2012.03.054

    Article  Google Scholar 

  8. Liu HK, Feng JX, Yang SQ, Jia T (2014) Wind power prediction model based on ARMA and improved BP-ANN. Adv Mater Res 1008:183–187. https://doi.org/10.4028/www.scientific.net/AMR.1008-1009.183

    Article  Google Scholar 

  9. Li H, Wang J, Lu H, Guo Z (2018) Research and application of a combined model based on variable weight for short term wind speed forecasting. Renew Energy 116:669–684. https://doi.org/10.1016/j.renene.2017.09.089

    Article  Google Scholar 

  10. Han Q, Meng F, Hu T, Chu F (2017) Non-parametric hybrid models for wind speed forecasting. Energy Convers Manage 148:554–568. https://doi.org/10.1016/j.enconman.2017.06.021

    Article  Google Scholar 

  11. Liu H, Tian H, Liang X, Li Y (2015) Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl Energy 157:183–194. https://doi.org/10.1016/j.apenergy.2015.08.014

    Article  Google Scholar 

  12. Wang J, Niu X, Liu Z, Zhang L (2020) Analysis of the influence of international benchmark oil price on China’s real exchange rate forecasting. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103783

    Article  Google Scholar 

  13. Zhang Y, Pan G, Chen B, Han J, Zhao Y, Zhang C (2020) Short-term wind speed prediction model based on GA-ANN improved by VMD. Renew Energy. https://doi.org/10.1016/j.renene.2019.12.047

    Article  Google Scholar 

  14. Ni YQ, Li M (2016) Wind pressure data reconstruction using neural network techniques: a comparison between BPNN and GRNN. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2016.04.049

    Article  Google Scholar 

  15. ArunKumar KE, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM (2021) Forecasting of COVID-19 Using deep layer recurrent neural networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) Cells, Chaos. Solitons Fractals. https://doi.org/10.1016/j.chaos.2021.110861

    Article  Google Scholar 

  16. Wang HZ, Wang GB, Li GQ, Peng JC, Liu YT (2016) Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl Energy. https://doi.org/10.1016/j.apenergy.2016.08.108

    Article  Google Scholar 

  17. Zhou J, Shi J, Li G (2011) Fine tuning support vector machines for short-term wind speed forecasting. Energy Convers Manage 52(4):1990–1998. https://doi.org/10.1016/j.enconman.2010.11.007

    Article  Google Scholar 

  18. Barbounis TG, Theocharis JB, Alexiadis MC, Dokopoulos PS (2006) Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans Energy Convers. https://doi.org/10.1109/TEC.2005.847954

    Article  Google Scholar 

  19. Liu Z, Jiang P, Wang J, Zhang L (2021) Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multiobjective version of mayfly optimization algorithm. Expert Syst. Appl. 177:114974. https://doi.org/10.1016/j.eswa.2021.114974

    Article  Google Scholar 

  20. Viswanathan S, Anand Kumar M, Soman KP (2019) A sequence-based machine comprehension modeling using LSTM and GRU. Lect Notes Electr Eng. https://doi.org/10.1007/978-981-13-5802-9_5

    Article  Google Scholar 

  21. Khodayar M, Wang J (2019) Spatio-temporal graph deep neural network for short-term wind speed forecasting. IEEE Trans Sustain Energy 10(2):670–681. https://doi.org/10.1109/TSTE.2018.2844102

    Article  Google Scholar 

  22. Chen MR, Zeng GQ, Lu KD, Weng J (2019) A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM. IEEE Internet Things J 6(4):6997–7010. https://doi.org/10.1109/JIOT.2019.2913176

    Article  Google Scholar 

  23. Wang J, Wang Y, Li Z, Li H, Yang H (2020) A combined framework based on data preprocessing, neural networks and multi-tracker optimizer for wind speed prediction. Sustain Energy Technol Assess 40:100757. https://doi.org/10.1016/j.seta.2020.100757

    Article  Google Scholar 

  24. Zhao M, Yuan Y, Li B, Nie L (2020) Trend prediction of ultrasonic grinding force of alumina ceramics based on fuzzy information granulation and optimized support vector machine. Aerospace Materials Technology 50(4):24–29. https://doi.org/10.12044/j.issn.1007-2330.2020.04.005

    Article  Google Scholar 

  25. Wang D, Luo H, Grunder O, Lin Y (2017) Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction. Renew Energy 113:1345–1358. https://doi.org/10.1016/j.renene.2017.06.095

    Article  Google Scholar 

  26. Niu X, Wang J (2019) A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting. Appl Energy 241:519–539. https://doi.org/10.1016/j.apenergy.2019.03.097

    Article  Google Scholar 

  27. Tian C, Hao Y (2018) A novel nonlinear combined forecasting system for short-term load forecasting. Energies 11(4):712

    Article  MathSciNet  Google Scholar 

  28. Lv M, Li J, Niu X, Wang J (2022) Novel deterministic and probabilistic combined system based on deep learning and self-improved optimization algorithm for wind speed forecasting. Sustain Energy Technol Assess 52:102186. https://doi.org/10.1016/j.seta.2022.102186

    Article  Google Scholar 

  29. Wang J, Lv M, Li Z, Zeng B (2023) Multivariate selection-combination short-term wind speed forecasting system based on convolution-recurrent network and multi-objective chameleon swarm algorithm. Exp Syst App 214:119129. https://doi.org/10.1016/j.eswa.2022.119129

    Article  Google Scholar 

  30. Zadeh LA (1979) Fuzzy sets and information granularity[J]. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers, pp 433–448

  31. Cui Y, Geng Z, Zhu Q, Han Y (2017) Review: Multi-objective optimization methods and application in energy saving. Energy 125:681–704. https://doi.org/10.1016/j.energy.2017.02.174

    Article  Google Scholar 

  32. Khoroshiltseva M, Slanzi D, Poli I (2016) A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices. Appl Energy 184:1400–1410. https://doi.org/10.1016/j.apenergy.2016.05.015

    Article  Google Scholar 

  33. Martín A, Schütze O (2018) Pareto Tracer: a predictor–corrector method for multi-objective optimization problems. Eng Optim 50(3):516–536. https://doi.org/10.1080/0305215X.2017.1327579

    Article  MathSciNet  Google Scholar 

  34. Coello CAC, Lechuga MS (2002) MOPSO: A proposal for multiple objective particle swarm optimization[C]//Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). IEEE 2:1051–1056. https://doi.org/10.1109/CEC.2002.1004388

  35. Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820. https://doi.org/10.1007/s10489-017-1019-8

    Article  Google Scholar 

  36. Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46(1):79–95. https://doi.org/10.1007/s10489-016-0825-8

    Article  Google Scholar 

  37. Mirjalili S, Saremi S, Mirjalili SM, dos Coelho LS (2016) Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119. https://doi.org/10.1016/j.eswa.2015.10.039

    Article  Google Scholar 

  38. Zhang Y, Gong D, Sun J, Qu B (2018) A decomposition-based archiving approach for multi-objective evolutionary optimization. Inf Sci 430–431:397–413. https://doi.org/10.1016/j.ins.2017.11.052

    Article  Google Scholar 

  39. Cai L, Qu S, Cheng G (2018) Two-archive method for aggregation-based many-objective optimization. Inf Sci 422:305–317. https://doi.org/10.1016/j.ins.2017.08.078

    Article  Google Scholar 

  40. Deb K, Agrawal S, Pratap A et al (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II[C]//Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, September 18–20, 2000 Proceedings 6. Springer Berlin Heidelberg, pp 849–858. https://doi.org/10.1007/3-540-45356-3_83

  41. Ahmadi A, Tiruta-Barna L, Capitanescu F, Benetto E, Marvuglia A (2016) An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: Application to process eco-design. Comput Chem Eng 87:95–110. https://doi.org/10.1016/j.compchemeng.2015.12.008

    Article  Google Scholar 

  42. Chen L, Li Q, Zhao X, Fang Z, Peng F, Wang J (2019) Multi-population coevolutionary dynamic multi-objective particle swarm optimization algorithm for power control based on improved crowding distance archive management in CRNs. Comput Commun 145:146–160. https://doi.org/10.1016/j.comcom.2019.06.009

    Article  Google Scholar 

  43. Yi W, Ning Z, Yushi T, Tao H, Kirschen Daniel S, Chongqing K (2019) Combining Probabilistic Load Forecasts. IEEE Trans Smart Grid 10(4):3664–74. https://doi.org/10.1109/TSG.516541110.1109/TSG.2018.2833869

    Article  Google Scholar 

  44. Chen H, Wan Q, Wang Y (2014) Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models. Energies 7(7):4185–4198. https://doi.org/10.3390/en7074185

    Article  Google Scholar 

  45. Lu P, Ye L, Zhong W, Qu Y, Zhai B, Tang Y, Zhao Y (2020) A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. J Clean Prod 254:119993. https://doi.org/10.1016/j.jclepro.2020.119993

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 71671029) and the National Natural Science Foundation of China (Grant No. 72074028).

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Authors and Affiliations

  1. School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China

    ShiWen Wang & Jianzhou Wang

  2. School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China

    Bo Zeng

  3. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Weigang Zhao

  4. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China

    Weigang Zhao

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  1. ShiWen Wang
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Correspondence to Jianzhou Wang.

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Wang, S., Wang, J., Zeng, B. et al. A structure for predicting wind speed using fuzzy granulation and optimization techniques. Appl Intell 54, 3859–3883 (2024). https://doi.org/10.1007/s10489-023-04906-9

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