Artificial bee colony algorithm–optimized error minimized extreme learning machine and its application in short-term wind speed prediction

In order to improve the prediction accuracy of short-term wind speed, a short-term wind speed prediction model based on artificial bee colony algorithm optimized error minimized extreme learning machine model is proposed. The extreme learning machine has the advantages of fast learning speed and strong generalization ability. But many useless neurons of incremental extreme learning machine have little influences on the final output and, at the same time, reduce the efficiency of the algorithm. The optimal parameters of the hidden layer nodes will make network output error of incremental extreme learning machine decrease with fast speed. Based on the error minimized extreme learning machine, artificial bee colony algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons, reduce training and prediction error, achieve the goal of reducing the network complexity, and improve the efficiency of the algorithm. The error minimized extreme learning machine prediction model is constructed with the obtained optimal parameters. The stability and convergence property of artificial bee colony algorithm optimized error minimized extreme learning machine model are proved. The practical short-term wind speed time series is used as the research object and to verify the validity of the prediction model. Multi-step prediction simulation of short-term wind speed is carried out. Compared with other prediction models, simulation results show that the prediction model proposed in this article reduces the training time of the prediction model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improves the performance indicators.

[1]  Henrik Madsen,et al.  Short-term probabilistic forecasting of wind speed using stochastic differential equations , 2016 .

[2]  Matthias Wächter,et al.  Stochastic modeling of lift and drag dynamics under turbulent wind inflow conditions , 2015 .

[3]  Robert K. L. Gay,et al.  Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.

[4]  Shahaboddin Shamshirband,et al.  Estimating building energy consumption using extreme learning machine method , 2016 .

[5]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[6]  Carlos Gershenson,et al.  Wind speed forecasting for wind farms: A method based on support vector regression , 2016 .

[7]  Sancho Salcedo-Sanz,et al.  Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach , 2014 .

[8]  Oliver Kramer,et al.  Short-Term Wind Power Prediction with Combination of Speed and Power Time Series , 2015, KI.

[9]  P. K. Dash,et al.  Short term wind speed prediction using multiple kernel pseudo inverse neural network , 2018, Int. J. Autom. Comput..

[10]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[11]  Nadja Saleck,et al.  Wind power forecast error smoothing within a wind farm , 2007 .

[12]  Mazhar Hussain Baloch,et al.  Stability and nonlinear controller analysis of wind energy conversion system with random wind speed , 2016 .

[13]  Akin Tascikaraoglu,et al.  A review of combined approaches for prediction of short-term wind speed and power , 2014 .

[14]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[15]  Sarat Kumar Sahoo,et al.  Renewable and sustainable energy reviews solar photovoltaic energy progress in India: A review , 2016 .

[16]  P. Ramasamy,et al.  Wind speed prediction in the mountainous region of India using an artificial neural network model , 2015 .

[17]  Niels Kjølstad Poulsen,et al.  Wind Speed Estimation and Parametrization of Wake Models for Downregulated Offshore Wind Farms within the scope of PossPOW Project , 2014 .

[18]  Xiaohua Xia,et al.  Short-term wind power prediction using Least-Square Support Vector Machines , 2012, IEEE Power and Energy Society Conference and Exposition in Africa: Intelligent Grid Integration of Renewable Energy Resources (PowerAfrica).

[19]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[20]  Yuan Lan,et al.  Two-stage extreme learning machine for regression , 2010, Neurocomputing.

[21]  Niels Kjølstad Poulsen,et al.  A simplified dynamic inflow model and its effect on the performance of free mean wind speed estimation , 2012 .

[22]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Oliver Kramer,et al.  Wind Power Ramp Event Prediction with Support Vector Machines , 2014, HAIS.

[24]  Wei Wang,et al.  Improved Convex Incremental Extreme Learning Machine Based on Enhanced Random Search , 2014 .

[25]  Sancho Salcedo-Sanz,et al.  Local models-based regression trees for very short-term wind speed prediction , 2015 .

[26]  Kodjo Agbossou,et al.  Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data , 2016 .

[27]  Seth D. Guikema,et al.  Probabilistic maximum-value wind prediction for offshore environments , 2015 .

[28]  Daniel Ambach,et al.  Wind Speed and Power Forecasting - A Review and Incorporating Asymmetric Loss , 2016, 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO).

[29]  Oliver Kramer,et al.  Machine learning ensembles for wind power prediction , 2016 .

[30]  Joachim Peinke,et al.  Stochastic Modelling of Wind Speed Power Production Correlations , 2006 .

[31]  Bijaya Ketan Panigrahi,et al.  A multiobjective framework for wind speed prediction interval forecasts , 2016 .

[32]  Yan Zhao,et al.  Short-term wind speed combined prediction for wind farms , 2017, 2017 IEEE International Conference on Mechatronics and Automation (ICMA).

[33]  Ali Selamat,et al.  A Review of Advances in Extreme Learning Machine Techniques and Its Applications , 2017 .

[34]  Guang-Bin Huang,et al.  Convex Incremental Extreme Learning Machine , 2007 .

[35]  Hassen Bouzgou.  A fast and accurate model for forecasting wind speed and solar radiation time series based on extreme learning machines and principal components analysis , 2014 .

[36]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[37]  Christopher Heard,et al.  Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model , 2016 .

[38]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[39]  Sancho Salcedo-Sanz,et al.  Short-term wind speed prediction in wind farms based on banks of support vector machines , 2011 .

[40]  C. W. Tong,et al.  Retraction Note to: Application of extreme learning machine for estimation of wind speed distribution , 2018, Climate Dynamics.

[41]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .