Abstract
With the development of the times, wind energy has increasingly become crucial energy, which has attracted the attention of researchers. To maximize the use of wind energy, the precision of wind speed prediction is very important. Naturally, the calculation of wind speed prediction accuracy has become a critical link in wind energy utilization, and also plays a decisive role in energy supply and management. However, in the process of wind speed prediction, the instability and variability of wind speed make the prediction particularly difficult. Many scholars have done a great deal of research and put forward corresponding methods to improve this situation, but the problem that a single model can solve is limited. In order to improve this vulnerability, this paper proposes a combined model founded on data preprocessing, linear prediction model, neural network, deep learning, and multi-objective optimization to overcome this problem. The predictive results prove that the proposed combined model in this paper has advantages and can effectively improve the prediction accuracy, and it is proved that the combined model proposed can reach Pareto optimal theoretically. To effectively analyze the volatility and uncertainty of the combined model, based on the distribution function theory, an interval prediction model is established in this paper, and the experimental predictive values show that it can increase forecast precision, enhance prediction stability and make up for the shortcomings of the single models.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability statement
The data that support this study are available from the first author upon reasonable request (Email address of the first author: 1207231431@qq.com).
References
An Y, Wang J, Lu H, Zhao W (2021) Research of a combined wind speed model based on multi-objective ant lion optimization algorithm. Int Trans Electr Energy Syst. https://doi.org/10.1002/2050-7038.13189
Chang X, Nie F, Wang S, et al (2015) Compound rank-k projections for bilinear analysis. https://doi.org/10.1109/TNNLS.2015.2441735
Chen K, Yao L, Zhang D et al (2020) A Semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans Neural Networks Learn Syst 31:1747–1756. https://doi.org/10.1109/TNNLS.2019.2927224
Cheng Z, Wang J (2020) A new combined model based on multi-objective salp swarm optimization for wind speed forecasting. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106294
Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. 1–9. https://doi.org/10.48550/arXiv.1412.3555
Dhiman G, Singh KK, Soni M et al (2021) MOSOA: a new multi-objective seagull optimization algorithm. Expert Syst Appl 167:114150. https://doi.org/10.1016/j.eswa.2020.114150
Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13:253. https://doi.org/10.2307/1392185
Ding S, Zhao H, Zhang Y et al (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44:103–115. https://doi.org/10.1007/s10462-013-9405-z
GWEC-Global-Wind-Report-2021(pdf) No Title (2022). https://gwec.net/global-wind-report-2021/
He Y, Yan Y, Xu Q (2019) Wind and solar power probability density prediction via fuzzy information granulation and support vector quantile regression. Int J Electr Power Energy Syst 113:515–527. https://doi.org/10.1016/j.ijepes.2019.05.075
Hwang JTG, Ding AA (1997) Prediction intervals for artificial neural networks. J Am Stat Assoc 92:748–757. https://doi.org/10.1080/01621459.1997.10474027
Jiang P, Li R, Lu H, Zhang X (2020a) Modeling of electricity demand forecast for power system. Neural Comput Appl 32:6857–6875. https://doi.org/10.1007/s00521-019-04153-5
Jiang P, Yang H, Li R, Li C (2020b) Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm. Appl Soft Comput J 92:106320. https://doi.org/10.1016/j.asoc.2020.106320
Jiang P, Liu Z, Niu X, Zhang L (2021) A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy 217:119361. https://doi.org/10.1016/j.energy.2020.119361
Li Z, Nie F, Chang X, et al (2018a) Rank-Constrained Spectral Clustering. https://doi.org/10.1109/TNNLS.2018a.2817538
Li Z, Nie F, Chang X et al (2018b) Dynamic affinity graph construction for spectral clustering using multiple features. IEEE Trans Neural Netw Learn Syst 29:6323–6332. https://doi.org/10.1109/TNNLS.2018.2829867
Li Z, Yao L, Chang X et al (2019) Zero-shot event detection via event-adaptive concept relevance mining. Pattern Recognit 88:595–603. https://doi.org/10.1016/j.patcog.2018.12.010
Lindsey JK (1974) Construction and comparison of statistical models. J R Stat Soc Ser B. https://doi.org/10.1111/j.2517-6161.1974.tb01015.x
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 multi-objective version of mayfly optimization algorithm. Expert Syst Appl 177:114974. https://doi.org/10.1016/j.eswa.2021.114974
Luo M, Chang X, Nie L et al (2018) An adaptive semisupervised feature analysis for video semantic recognition. IEEE Trans Cybern 48:648–660. https://doi.org/10.1109/TCYB.2017.2647904
Luo L, Li H, Wang J, Hu J (2021) Design of a combined wind speed forecasting system based on decomposition-ensemble and multi-objective optimization approach. Appl Math Model 89:49–72. https://doi.org/10.1016/j.apm.2020.07.019
Lv M, Li J, Niu X, Wang J (2022a) 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
Lv M, Wang J, Niu X, Lu H (2022b) A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03595-x
Meraihi Y, Ramdane-Cherif A, Acheli D, Mahseur M (2020) Dragonfly algorithm: a comprehensive review and applications. Neural Comput Appl 32:16625–16646. https://doi.org/10.1007/s00521-020-04866-y
Mirjalili S, Gandomi AH, Mirjalili SZ et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Nie Y, Jiang P, Zhang H (2020) A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106809
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
Ren C, An N, Wang J et al (2014) Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2013.11.015
Shao Y, Wang J, Zhang H, Zhao W (2021) An advanced weighted system based on swarm intelligence optimization for wind speed prediction. Appl Math Model 100:780–804. https://doi.org/10.1016/j.apm.2021.07.024
Song J, Wang J, Lu H (2018) A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Appl Energy 215:643–658. https://doi.org/10.1016/j.apenergy.2018.02.070
Wang T, Zhang M, Yu Q, Zhang H (2012) Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal. J Appl Geophys 83:29–34. https://doi.org/10.1016/j.jappgeo.2012.05.002
Wang J, Wang Y, Li Z et al (2020a) 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
Wang J, Zhang L, Niu X, Liu Z (2020b) Effects of PM2.5 on health and economic loss: evidence from Beijing–Tianjin–Hebei region of China. J Clean Prod 257:120605. https://doi.org/10.1016/j.jclepro.2020b.120605
Wang Y, Wang J, Li Z (2020c) A novel hybrid air quality early-warning system based on phase-space reconstruction and multi-objective optimization: a case study in China. J Clean Prod 260:121027. https://doi.org/10.1016/j.jclepro.2020.121027
Wang J, Li J, Li Z (2022a) Prediction of Air pollution interval based on data preprocessing and multi-objective dragonfly optimization algorithm. Front Ecol Evol. https://doi.org/10.3389/fevo.2022.855606
Wang J, Zhang L, Li Z (2022b) Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm. Appl Energy. https://doi.org/10.1016/j.apenergy.2021.117911
Yan C, Chang X, Luo M et al (2021) Self-weighted robust LDA for multiclass classification with edge classes. ACM Trans Intell Syst Technol 12:1–19. https://doi.org/10.1145/3418284
Yang W, Wang J, Niu T, Du P (2020) A novel system for multi-step electricity price forecasting for electricity market management. Appl Soft Comput J 88:106029. https://doi.org/10.1016/j.asoc.2019.106029
Yu E, Ma J, Sun J, et al (2022) Deep discrete cross-modal hashing with multiple supervision. Neurocomputing 486:215–224. https://doi.org/10.1016/j.neucom.2021.11.035
Yuan D, Chang X, Li Z, He Z (2022) Learning adaptive spatial-temporal context-aware correlation filters for UAV tracking. ACM Trans Multimed Comput Commun Appl 18:1–18. https://doi.org/10.1145/3486678
Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–127. https://doi.org/10.1016/S0165-0114(97)00077-8
Zeiler A, Faltermeier R, Keck IR et al (2010) Empirical mode decomposition—an introduction. Proc Int Jt Conf Neural Netw. https://doi.org/10.1109/IJCNN.2010.5596829
Zhang X, Miao Q, Zhang H, Wang L (2018) A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mech Syst Signal Process 108:58–72. https://doi.org/10.1016/j.ymssp.2017.11.029
Zhang D, Yao L, Chen K et al (2020) Making sense of spatio-temporal preserving representations for EEG-based human intention recognition. IEEE Trans Cybern 50:3033–3044. https://doi.org/10.1109/TCYB.2019.2905157
Zhou R, Chang X, Shi L et al (2020) Person reidentification via multi-feature fusion with adaptive graph learning. IEEE Trans Neural Netw Learn Syst 31:1592–1601. https://doi.org/10.1109/TNNLS.2019.2920905
Zhu Y, Yousefi N (2021) Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm. Int J Hydrogen Energy 46:9541–9552. https://doi.org/10.1016/j.ijhydene.2020.12.107
Zhu S, Lian X, Wei L et al (2018) PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors. Atmos Environ 183:20–32. https://doi.org/10.1016/j.atmosenv.2018.04.004
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No. 72074028), and the National Natural Science Foundation of China (No. 71671029).
Author information
Authors and Affiliations
Contributions
JL: Methodology, Software, Writing—Original Draft, Writing—Review & Editing. JW: Conceptualization, Funding acquisition, Supervision. SW: Visualization, Validation. WZ: Data curation, Project administration, Formal analysis.
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, J., Wang, J., Wang, S. et al. Wind speed point prediction and interval prediction method based on linear prediction model, neural network, and deep learning. J Ambient Intell Human Comput 14, 9207–9216 (2023). https://doi.org/10.1007/s12652-022-04423-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-022-04423-6