Multi-objective algorithm for the design of prediction intervals for wind power forecasting model

Abstract A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width.

[1]  Ioannis P. Panapakidis,et al.  Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model , 2017 .

[2]  Erasmo Cadenas,et al.  Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México , 2015, Neural Computing and Applications.

[3]  Murat Kankal,et al.  Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey , 2017, Neural Computing and Applications.

[4]  Jianzhou Wang,et al.  Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China. , 2017, Environmental pollution.

[5]  I. Jacob Raglend,et al.  Sequential wavelet-ANN with embedded ANN-PSO hybrid electricity price forecasting model for Indian energy exchange , 2015, Neural Computing and Applications.

[6]  Lifeng Wu,et al.  Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions , 2018 .

[7]  Mohammed Imamul Hassan Bhuiyan,et al.  Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating , 2016, Biomed. Signal Process. Control..

[8]  Xin Li,et al.  Short-term wind speed prediction using an extreme learning machine model with error correction , 2018 .

[9]  Chen Li,et al.  Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting , 2018, Measurement.

[10]  Yu Jin,et al.  The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model , 2018, Inf. Sci..

[11]  Shaolong Sun,et al.  A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting , 2018 .

[12]  Henrik Madsen,et al.  Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information , 2014, IEEE Transactions on Smart Grid.

[13]  Wei Qiao,et al.  Short-Term Wind Power Prediction Using a Wavelet Support Vector Machine , 2012, IEEE Transactions on Sustainable Energy.

[14]  Georgios Giasemidis,et al.  A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting , 2016, 1610.05183.

[15]  Salim Lahmiri,et al.  Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression , 2018, Appl. Math. Comput..

[16]  Qinghua Hu,et al.  Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .

[17]  Ricardo J. Bessa,et al.  On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power , 2016 .

[18]  Lei Ye,et al.  Multi-objective optimization for construction of prediction interval of hydrological models based on ensemble simulations , 2014 .

[19]  Zhongyi Hu,et al.  Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework , 2014, ArXiv.

[20]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[21]  Johan A. K. Suykens,et al.  Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression , 2011, IEEE Transactions on Neural Networks.

[22]  Xiaobing Kong,et al.  Wind speed prediction using reduced support vector machines with feature selection , 2015, Neurocomputing.

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

[24]  Ping Jiang,et al.  Research and Application of a New Hybrid Wind Speed Forecasting Model on BSO Algorithm , 2017 .

[25]  Liping Xie,et al.  Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework , 2016, Neurocomputing.

[26]  Ponnuthurai Nagaratnam Suganthan,et al.  Ensemble methods for wind and solar power forecasting—A state-of-the-art review , 2015 .

[27]  Feng Liu,et al.  A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting , 2017 .

[28]  Haiyan Lu,et al.  A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting , 2018 .

[29]  Zijun Zhang,et al.  Short-term wind speed forecasting with Markov-switching model , 2014 .

[30]  Jianzhou Wang,et al.  Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting , 2017 .

[31]  Ponnuthurai N. Suganthan,et al.  Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting , 2018, Knowl. Based Syst..

[32]  P. Bhaskaran,et al.  Performance of WRF-ARW winds on computed storm surge using hydodynamic model for Phailin and Hudhud cyclones , 2017 .

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

[34]  Chu Zhang,et al.  A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting , 2017 .

[35]  J. Lelieveld,et al.  Intercomparison of boundary layer parameterizations for summer conditions in the eastern Mediterranean island of Cyprus using the WRF - ARW model , 2017, Atmospheric Research.

[36]  Jian Luo,et al.  Benchmarking robustness of load forecasting models under data integrity attacks , 2018 .

[37]  Chen Jie,et al.  Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization , 2018, Energy Conversion and Management.

[38]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[39]  V. Monbet,et al.  Non-homogeneous hidden Markov-switching models for wind time series , 2015 .

[40]  Bijaya K. Panigrahi,et al.  Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines , 2015, IEEE Transactions on Industrial Informatics.

[41]  Ranran Li,et al.  A wind speed interval prediction system based on multi-objective optimization for machine learning method , 2018, Applied Energy.

[42]  Fulei Chu,et al.  Non-parametric hybrid models for wind speed forecasting , 2017 .

[43]  Yu Jin,et al.  A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting , 2017, Appl. Soft Comput..

[44]  Henrik Ohlsson,et al.  A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014 , 2016 .

[45]  Huisheng Zhang,et al.  A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation , 2016 .

[46]  Inés María Galván,et al.  Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks , 2017, Inf. Sci..

[47]  Matthias Ritter,et al.  Forecasting volatility of wind power production , 2016 .

[48]  Achilleas Zapranis,et al.  Wind Derivatives: Modeling and Pricing , 2012, Computational Economics.

[49]  Abbas Khosravi,et al.  Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .

[50]  Dexuan Zou,et al.  On the iterative convergence of harmony search algorithm and a proposed modification , 2014, Appl. Math. Comput..

[51]  Abbas Khosravi,et al.  Particle swarm optimization for construction of neural network-based prediction intervals , 2014, Neurocomputing.

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

[53]  Fotios Petropoulos,et al.  Exploring the sources of uncertainty: Why does bagging for time series forecasting work? , 2018, Eur. J. Oper. Res..

[54]  Jianwu Dang,et al.  Fuzzy rough regression with application to wind speed prediction , 2014, Inf. Sci..

[55]  T. Giannaros,et al.  Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece , 2017 .

[56]  R. Velo,et al.  Wind speed estimation using multilayer perceptron , 2014 .

[57]  Menglin Zhang,et al.  A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction , 2017 .

[58]  James Stephen Marron,et al.  Regression smoothing parameters that are not far from their optimum , 1992 .

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

[60]  Okyay Kaynak,et al.  Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting , 2017, IEEE Transactions on Industrial Informatics.

[61]  Alison S. Tomlin,et al.  A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data , 2017 .

[62]  Zaccheus O. Olaofe,et al.  A 5-day wind speed & power forecasts using a layer recurrent neural network (LRNN) , 2014 .

[63]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[64]  D. P. Kothari,et al.  Stochastic economic emission load dispatch , 1993 .

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

[66]  Xuejun Chen,et al.  A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting , 2018, Energies.

[67]  Qingli Dong,et al.  A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China , 2017 .

[68]  Ricardo Nicolau Nassar Koury,et al.  Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system , 2018 .

[69]  M. Anghel,et al.  Continuous wind speed models based on stochastic differential equations , 2013 .

[70]  Chaoqing Yuan,et al.  Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model , 2015 .

[71]  Carlos González-Mingueza,et al.  RETRACTED: Wind prediction using Weather Research Forecasting model (WRF): A case study in Peru , 2014 .

[72]  Peng-Yeng Yin,et al.  A power-deficiency and risk-management model for wind farm micro-siting using cyber swarm algorithm , 2016 .

[73]  Rob J Hyndman,et al.  Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond , 2016 .

[74]  Jie Wu,et al.  Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model , 2013 .