A self-adaptive kernel extreme learning machine for short-term wind speed forecasting

Abstract Wind speed forecasting with artificial neural networks (ANNs) plays important role in safely utilizing and integrating the wind power. With the rapid updated wind speed data, however, the only way to guarantee forecasting accuracy for these ANN models is re-training from scratch with an updated training dataset. Obviously, it is an inefficient work due to the resumption of constructing the new training dataset and re-training the model. To enhance training efficiency, reduce re-training cost and improve forecasting accuracy, a self-adaptive kernel extreme learning machine (KELM) is proposed in this paper. With an advanced and efficient learning process, the self-adaptive KELM could simultaneously obsolete old data and learn from new data by reserving overlapped information between the updated and old training datasets. To evaluate the efficiency and accuracy of the self-adaptive KELM, the wind speed data from three different stations are employed as a numerical experiment. The Mean Absolute Error (MAE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) showed that the self-adaptive KELM with a simple structure could obtain more accurate forecasting results at a faster calculation speed than comparison models, where the proposed model decreased the MAPE values with 7.4776%, 3.5329% and 2.0900% in 1-step, 3-step and 5-step forecasting, respectively

[1]  Kun Guo,et al.  Forecasting China's Service Outsourcing Development with an EMD-VAR-SVR Ensemble Method☆ , 2016 .

[2]  Lei Wang,et al.  Multiple kernel extreme learning machine , 2015, Neurocomputing.

[3]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[4]  Lei Wu,et al.  Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .

[5]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[6]  Wan-Yu Deng,et al.  Cross-person activity recognition using reduced kernel extreme learning machine , 2014, Neural Networks.

[7]  Erasmo Cadenas,et al.  Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model , 2010 .

[8]  Chao Wang,et al.  Distributed Extreme Learning Machine with kernels based on MapReduce , 2015, Neurocomputing.

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

[10]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .

[11]  Jianzhou Wang,et al.  Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system , 2018 .

[12]  Feng Qian,et al.  Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm , 2017 .

[13]  Jing Ma,et al.  Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting , 2017 .

[14]  Rui Wang,et al.  Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting , 2017, Entropy.

[15]  Chi-Man Vong,et al.  Fast detection of impact location using kernel extreme learning machine , 2014, Neural Computing and Applications.

[16]  Jie Zhang,et al.  A data-driven multi-model methodology with deep feature selection for short-term wind forecasting , 2017 .

[17]  Jian Su,et al.  A novel bidirectional mechanism based on time series model for wind power forecasting , 2016 .

[18]  Kameshwar Poolla,et al.  Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform , 2016 .

[19]  Yong-Ping Zhao,et al.  Parsimonious kernel extreme learning machine in primal via Cholesky factorization , 2016, Neural Networks.

[20]  T. Cockerill,et al.  Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data , 2018, Renewable Energy.

[21]  Asifullah Khan,et al.  Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks , 2017 .

[22]  Mohamed Mohandes,et al.  Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS) , 2011 .

[23]  Chen Hua-you.  Research on Superior Combination Forecasting Model Based on Forecasting Effective Measure , 2002 .

[24]  Jun Wang,et al.  Forecasting stochastic neural network based on financial empirical mode decomposition , 2017, Neural Networks.

[25]  Eric M. Aldrich,et al.  Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center , 2006 .

[26]  Abheejeet Mohapatra,et al.  Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting , 2019, Renewable Energy.

[27]  Olivier Chapelle,et al.  Training a Support Vector Machine in the Primal , 2007, Neural Computation.

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

[29]  J. Mercer.  Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .

[30]  A. Immanuel Selvakumar,et al.  Linear and non-linear autoregressive models for short-term wind speed forecasting , 2016 .

[31]  Prashant Baredar,et al.  Solar–wind hybrid renewable energy system: A review , 2016 .

[32]  Xiaoming Zha,et al.  Wind power prediction method based on regime of switching kernel functions , 2016 .

[33]  Shantha Gamini Jayasinghe,et al.  A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system , 2017 .

[34]  Jianzhou Wang,et al.  A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm , 2018 .

[35]  Qinghua Hu,et al.  Robust functional regression for wind speed forecasting based on Sparse Bayesian learning , 2019, Renewable Energy.

[36]  Bernhard Schölkopf,et al.  A Generalized Representer Theorem , 2001, COLT/EuroCOLT.

[37]  Jing Zhao,et al.  Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method , 2017 .

[38]  Jianzhou Wang,et al.  Short-term wind speed forecasting using a hybrid model , 2017 .

[39]  Heping Liu,et al.  Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed , 2011 .

[40]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[41]  Yitao Liu,et al.  Deep belief network based deterministic and probabilistic wind speed forecasting approach , 2016 .

[42]  Nabil Benoudjit,et al.  Multiple architecture system for wind speed prediction , 2011 .

[43]  Li Li,et al.  Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system , 2019, Applied Energy.

[44]  Haidar Samet,et al.  A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting , 2014, Expert Syst. Appl..

[45]  Liye Xiao,et al.  A secondary decomposition based hybrid structure with meteorological analysis for deterministic and probabilistic wind speed forecasting , 2019, Appl. Soft Comput..

[46]  Jianzhou Wang,et al.  A novel hybrid system based on a new proposed algorithm-Multi-Objective Whale Optimization Algorithm for wind speed forecasting , 2017 .