A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm

Owing to the complexity and uncertainty of wind speed, accurate wind speed prediction has become a highly anticipated and challenging problem in recent years. Researchers have conducted numerous studies on wind speed prediction theory and practice; however, research on multi-step wind speed prediction remains scarce, which hinders further development in this area. To improve upon the accuracy and stability of multi-step wind speed prediction, this paper proposes a combination model based on a data preprocessing strategy, an improved optimization model, a no negative constraint theory, and several single prediction models. To improve upon forecasting performance, an improved water cycle algorithm based on a quasi-Newton algorithm is proposed to optimize the weight coefficients of the single models. In the empirical research, 10-min and 30-min wind speed data from Shandong Province in China, collected for case studies, were used to assess the comprehensive performance of the proposed combination model. Finally, we used 10-fold cross-validation and multiple error criteria to evaluate the comprehensive performance of the proposed combination model. The simulation results indicate that (a) the quasi-Newton algorithm can effectively increase the diversity of the water cycle algorithm particles, resulting in improved water cycle algorithm optimization performance; (b) the combination model exhibits superior predictive performance to a single model by taking advantage of each single model; and (c) the proposed combination model can effectively improve multi-step wind speed prediction results.

[1]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[2]  Jianzhou Wang,et al.  Combined forecasting models for wind energy forecasting: A case study in China , 2015 .

[3]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

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

[6]  Tarlochan Kaur,et al.  Application of artificial neural network for short term wind speed forecasting , 2016, 2016 Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE).

[7]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[8]  F. Cassola,et al.  Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output , 2012 .

[9]  Jianzhou Wang,et al.  A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting , 2015 .

[10]  Chen Wang,et al.  Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting , 2017 .

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

[12]  A. Testa,et al.  Markov chain modeling for very-short-term wind power forecasting , 2015 .

[13]  Mario Vasak,et al.  Deep neural networks for ultra-short-term wind forecasting , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[14]  Mohammad Monfared,et al.  A new strategy for wind speed forecasting using artificial intelligent methods , 2009 .

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

[16]  C. G. Broyden.  The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations , 1970 .

[17]  Chao Ren,et al.  Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting , 2014, Knowl. Based Syst..

[18]  P. S. Dokopoulos,et al.  Wind speed and power forecasting based on spatial correlation models , 1999 .

[19]  Na Zhang,et al.  A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network , 2013, 2013 IEEE Power & Energy Society General Meeting.

[20]  Mehrdad Abedi,et al.  Short term wind speed forecasting for wind turbine applications using linear prediction method , 2008 .

[21]  Jing Zhao,et al.  An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed , 2016 .

[22]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[23]  N. D. Hatziargyriou,et al.  Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks , 2012, IEEE Transactions on Power Systems.

[24]  Ismael Sánchez,et al.  Short-term prediction of wind energy production , 2006 .

[25]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

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

[27]  Ning An,et al.  Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting , 2013 .

[28]  Joao P. S. Catalao,et al.  Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information , 2015 .

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

[30]  Y. Noorollahi,et al.  Using artificial neural networks for temporal and spatial wind speed forecasting in Iran , 2016 .

[31]  Jorge Nocedal,et al.  On the Behavior of Broyden's Class of Quasi-Newton Methods , 1992, SIAM J. Optim..

[32]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[33]  Z. Tan,et al.  Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models , 2010 .

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

[35]  Maria Grazia De Giorgi,et al.  Assessment of the benefits of numerical weather predictions in wind power forecasting based on stati , 2011 .

[36]  Tao Xue-feng.  Short-Term Wind Speed Forecasting Combing Time Series and Neural Network Method , 2008 .

[37]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[38]  Yi-Ming Wei,et al.  One day ahead wind speed forecasting: A resampling-based approach , 2016 .

[39]  Andrew Kusiak,et al.  Very short-term wind speed forecasting with Bayesian structural break model , 2013 .

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

[41]  Mauricio A. Álvarez,et al.  Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison , 2015 .

[42]  Ping Jiang,et al.  A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms , 2016 .

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

[44]  Mustafa Inalli,et al.  Performance prediction of a ground-coupled heat pump system using artificial neural networks , 2008, Expert Syst. Appl..

[45]  Robert P. Broadwater,et al.  Current status and future advances for wind speed and power forecasting , 2014 .