Abstract
Short-term wind speed prediction plays a significant role in the management of large-scale wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and non-linearity of wind. For this purpose, a broad learning system (BLS) with ensemble and classification named BLS-EC is proposed to predict multi-step-ahead wind speed. The proposed method is based on a new neural network termed the BLS, which could work out the complex non-linear relation by learning model while ensuring the computational efficiency. To overcome the randomness and instability of a single BLS, this paper proposes the BLS ensemble method to improve the generalization and stability of the network. In order to improve the accuracy of prediction, a method called classification-guided regression is proposed to distinguish different variation patterns of initial predicted wind speed. According to the classification result, different pattern sequences are re-predicted to obtain the final prediction result. Applying this thinking and method into research of three real-time wind speed datasets which were taken from Sotavento Galicia SA (SG), Alberta (ALB), and Newfoundland (NFL), the validity and practical value of this method can be demonstrated. Results obtained clearly show that BLS is better than existing methods ARIMA and RBF. Moreover, the BLS-EC method improved generalization performance and the predicting precision of a single BLS. In this study, the BLS-EC was proposed and successfully applied to wind speed prediction.










Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Khosravi A, Koury RNN, Machado L. Thermo-economic analysis and sizing of the components of an ejector expansion refrigeration system. Int J Refrig 2018;86:463–479.
Song J, Wang J, Lu H. A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Appl Energy 2018;215:643–658.
Khosravi A, Koury RNN, Machado L, Pabon JJG. Energy, exergy and economic analysis of a hybrid renewable energy with hydrogen storage system. Energy. 2018;148:1087–1102.
Zuluaga CD, Alvarez MA, Giraldo E. Short-term wind speed prediction based on robust Kalman filtering: an experimental comparison. Appl Energy 2015;156:321–330.
El-Fouly THM, El-Saadany EF, Salama MMA. A study of wind farms output power prediction techniques. Proceedings north american power symposium; 2004. p. 249–254.
Lei M, Shiyan L, Chuanwen J, Hongling L, Yan Z. A review on the forecasting of wind speed and generated power. Renew Sustain Energy Rev 2009;13(4):915–920.
Kusiak A, Zheng H, Song Z. Wind farm power prediction: a data-mining approach. Wind Energy 2009; 12(3):275–293.
Kuik GV, Ummels B, Hendriks R. Sustainable energy technologies. Amsterdam: Springer; 2007.
Huang Z, Chalabi ZS. Use of time-series analysis to model and forecast wind speed. J Wind Eng Ind Aerodyn 1995;56:311–322.
Kamal J, Jafri YZ. Time series models to simulate and forecast hourly averaged wind speed in Quetta. Sol Energy 1997;61:23–32.
Sfetsos A. A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renew Energy 2000;21:23–35.
More A, Deo DC. Forecasting wind with neural networks. Marine Struct 2003;16(1):35–49.
Thordarson FO. Conditional weighted combination of wind power forecasts. Wind Energy 2011;13(8):751–63.
Giorgi MGD. Error analysis of short term wind power prediction models. Appl Energy 2011;88(4):1298–1311.
Ranganayaki V, Deepa SN. Linear and non-linear proximal support vector machine classifiers for wind speed prediction. Cluster Comput 2019;22:5379–5390.
Costa M, Pasero E. Artificial neural systems for verglass forecast. International joint conference on neural networks; 2001. p. 258–262.
Alexiadis MC, Dokopoulos PS, Sahsamanoglou HS, Manousaridis IM. Short-term forecasting of wind speed and related electric power. Sol Energy 1998;63(1):61–68.
Li S, Wunsch DC, Hair EO, Giesselmann MG. Neural network for wind power generation with compressing function. International conference on neural networks; 1997. p. 115–120.
Damoisis IG, Alexiadis MC, Theocharis JB, Dokopoulos PS. A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans Energy Convers 2004;19(2):352–361.
Katsigiannis YA, Tsikalakis AG, Georgilakis PS, Hatziargyriou ND. Improved wind power forecasting using a combined neuro-fuzzy and artificial neural network model. Advances in artificial intelligence, 4th Helenic conference on AI, SETN 2006, Heraklion, Crete, Greece, May 18-20; 2006. p. 105–115.
Hu QH, Su PY, Yu DR, Liu JF. Pattern-based wind speed prediction based on generalized principal component analysis. IEEE Trans Sustain Energ 2014;5(3):866–874.
Li Y, Yang P, Wang HJ. 2018. Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM. Cluster Comput. https://doi.org/10.1007/s10586-017-1422-2.
Chen CLP, Liu ZL. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans Neural Netw Learn Syst 2018;29(1):10–24.
Chevillon G. Direct multi-step estimation and forecasting. J Econ Surveys 2001;21:746–785.
Cox DR. Prediction by exponentially weighted moving averages and related methods. J R Stat Soc B 1961;23: 414–422.
Bontempi G. Long term time series prediction with multi-input multi-output local learning. Second European symposium on time series prediction 2008; 2008. p. 145–154.
Xu M, Han M, Chen CLP, Qiu T. 2018. Recurrent broad learning systems for time series prediction. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2018.2863020.
Han M, Feng S, Chen CLP, Xu M, Qiu T. 2018. Structured manifold broad learning system: a manifold perspective for large-scale chaotic time series analysis and prediction. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2018.2866149.
Hansen LK, Salamon P. Neural network ensembles. IEEE Trans Pattern Analy Mach Intell 1990;12(10): 993–1001.
Liu N, Sakamoto JT, Cao J, Koh ZX, Ho AFW, Lin ZP, Ong MEH. Ensemble-based risk scoring with extreme learning machine for prediction of adverse cardiac events. Cogn Comput 2017;9:545–554.
Dietterich T. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Mach Learn 2000;40(2):139–157.
Menke W, Menke J. Patterns suggested by data, Environmental data analysis with Matlab, 2nd ed. Cambridge: Academic Press; 2016, pp. 165–185.
Uhlmann E, Pontes RP, Laghmouchi A, Bergmann A. Intelligent pattern recognition of a SLM machine process and sensor data. Procedia CIRP 2017;464-469:62.
Theodoridis S, Koutroumbas K. Chapter 1-Introduction. Pattern Recog, 4th ed. Cambridge: Academic Press; 2009, pp. 1–12.
Jain AK. Data clustering: 50 years beyond k-means. Pattern Recog Lett 2010;31(8):651–666.
Huang G-B, Zhu Q-Y, Siew C-K2. Extreme learning machine: theory and applications. Neurocomputing 2006;70(1-3):489–501.
Guo T, Zhang L, Tan XH. Neuron pruning-based discriminative extreme learning machine for pattern classification. Cogn Comput 2017;9:581–595.
Savitha R, Suresh S, Kim HJ. A meta-cognitive learning algorithm for an extreme learning machine classifier. Cogn Comput 2014;6:253–263.
Li N, Yu Y, Zhou ZH. Diversity regularized ensemble pruning. ECML PKDD’12; 2012. p. 330–345.
Wang F, Mi Z, Su S, Zhao H. Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies. 2012;5:1355–1370.
Funding
The work was financially supported by the National Key R&D Program of China under Grant 2017YFC1501301; the Natural Science Foundation of China under Grants 61876219, 61503144, 61673188, and 61761130081; the Excellent Dissertation Cultivation Funds of Wuhan University of Technology (2018-YS-066); and the Natural Science Foundation of Hubei Province of China under Grant 2017CFB519.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflicts of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhu, L., Lian, C., Zeng, Z. et al. A Broad Learning System with Ensemble and Classification Methods for Multi-step-ahead Wind Speed Prediction. Cogn Comput 12, 654–666 (2020). https://doi.org/10.1007/s12559-019-09698-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-019-09698-0