Wind Power Prediction Based on PSO-SVR and Grey Combination Model

As a kind of green, clean and renewable energy, wind power generation has been widely utilized in various countries in the world. With the rapid development of wind energy, it is also facing prominent problems. Because wind power generation is intermittent, unstable and stochastic, it has caused serious difficulties for power grid dispatch. At present, the important method to solve this problem is to predict wind speed and wind power. Grey model is suitable for uncertain systems with poor information and needs less operation data, so it can be used for wind speed and wind power prediction. However, the traditional grey system model has the disadvantage of low prediction accuracy. Therefore, firstly the GM (1,1) for wind speed prediction is improved by background value optimization in this paper. In order to comprehensively reveal the inherent uncertainty of wind speed random series, the fractional order grey system models with different orders are constructed. Secondly, in order to overcome the shortcoming of single grey model, each grey model is effectively united, and a combination prediction model based on neural network is presented. The two NWP outputs, i.e. ECMWF and GRAPES-MESO, have been added to the prediction model for reducing the uncertainty. The structure parameters of the neural network are optimized by trial and error. Thirdly, the support vector regression model is established to fit the scatter operation data of wind speed-power, and the parameters of the model are optimized by the particle swarm algorithm. Then the power prediction value is obtained by the fitted wind speed-power relationship and the corresponding result of the grey combination model for wind speed prediction. Finally, wind speed and wind power are predicted based on the actual operation data. In addition, the prediction model based on ARIMA is also constructed as a benchmark model. The results show that the proposed grey combination model improves the prediction accuracy.

[1]  Gang Zheng,et al.  Ultra-Short Term Wind Power Prediction Model Based on Modified Grey Model Method for Power Control in Wind Farm , 2011 .

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

[3]  A. Khosravi,et al.  Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil , 2018 .

[4]  Yanbin Yuan,et al.  Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine , 2017 .

[5]  Eugene Fernandez,et al.  Analysis of wind power generation and prediction using ANN: A case study , 2008 .

[6]  Jianzhou Wang,et al.  A novel hybrid model for short-term wind power forecasting , 2019, Appl. Soft Comput..

[7]  Chao-Ming Huang,et al.  Short-term wind power forecasting and uncertainty analysis using a hybrid intelligent method , 2017 .

[8]  Jiani Heng,et al.  Research and application of a hybrid model based on Meta learning strategy for wind power deterministic and probabilistic forecasting , 2018, Energy Conversion and Management.

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

[10]  Xiaoxia Qi,et al.  Deep belief network based k-means cluster approach for short-term wind power forecasting , 2018, Energy.

[11]  Min Gan,et al.  Design a Wind Speed Prediction Model Using Probabilistic Fuzzy System , 2012, IEEE Transactions on Industrial Informatics.

[12]  Joachim Denzler,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[13]  Enrico Zio,et al.  An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Ladislav Zjavka,et al.  Wind speed forecast correction models using polynomial neural networks , 2015 .

[15]  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.

[16]  Ángel M. Pérez-Bellido,et al.  Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction , 2009 .

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

[18]  Yanbin Li,et al.  Short-term prediction of the output power of PV system based on improved grey prediction model , 2014, Proceedings of the 2014 International Conference on Advanced Mechatronic Systems.

[19]  Yongqian Liu,et al.  Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features , 2014, IEEE Transactions on Smart Grid.

[20]  Richard G. J. Flay,et al.  Wind direction forecasting with artificial neural networks and support vector machines , 2015 .

[21]  Hamidreza Zareipour,et al.  A review and discussion of decomposition-based hybrid models for wind energy forecasting applications , 2019, Applied Energy.

[22]  Ercan E. Kuruoglu,et al.  One-day ahead wind speed/power prediction based on polynomial autoregressive model , 2017 .

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

[24]  Ehab E. Elattar.  Prediction of wind power based on evolutionary optimised local general regression neural network , 2014 .

[25]  İnci Okumuş,et al.  Current status of wind energy forecasting and a hybrid method for hourly predictions , 2016 .

[26]  Enrico Zio,et al.  Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Li Li,et al.  A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data , 2018, Renewable Energy.

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

[29]  Haiyan Lu,et al.  A case study on a hybrid wind speed forecasting method using BP neural network , 2011, Knowl. Based Syst..

[30]  Khaled M. Abo-Al-Ez,et al.  A hybrid neuro-fuzzy power prediction system for wind energy generation , 2016 .

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

[32]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[33]  Jianzhou Wang,et al.  A self-adaptive hybrid approach for wind speed forecasting , 2015 .

[34]  Birgitte Bak-Jensen,et al.  ARIMA-Based Time Series Model of Stochastic Wind Power Generation , 2010, IEEE Transactions on Power Systems.

[35]  Duehee Lee,et al.  Short-Term Wind Power Ensemble Prediction Based on Gaussian Processes and Neural Networks , 2014, IEEE Transactions on Smart Grid.

[36]  Nasrudin Abd Rahim,et al.  Using data-driven approach for wind power prediction: A comparative study , 2016 .

[37]  Chi Dezhong,et al.  Program of Wind Speed Prediction Based on Numerical Simulation with Intelligent Optimization Algorithm , 2012 .

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

[39]  刘峰,et al.  An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed , 2015 .

[40]  Saeid Nahavandi,et al.  A New Fuzzy-Based Combined Prediction Interval for Wind Power Forecasting , 2016, IEEE Transactions on Power Systems.

[41]  Venkata Dinavahi,et al.  Direct Interval Forecast of Uncertain Wind Power Based on Recurrent Neural Networks , 2018, IEEE Transactions on Sustainable Energy.

[42]  Sancho Salcedo-Sanz,et al.  Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks , 2009, Neurocomputing.

[43]  Can Wan,et al.  Probabilistic forecasting of wind power generation using extreme learning machine. , 2013 .

[44]  Xiaoyu Yang,et al.  A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines , 2019, Applied Mathematical Modelling.

[45]  William A. Gallus,et al.  A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height , 2013 .

[46]  Mehrdad Saif,et al.  Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production , 2017 .

[47]  N. Safari,et al.  Very Short-Term Wind Power Prediction Interval Framework via Bi-Level Optimization and Novel Convex Cost Function , 2019, IEEE Transactions on Power Systems.

[48]  Paras Mandal,et al.  A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.

[49]  Ajit Achuthan,et al.  Recursive wind speed forecasting based on Hammerstein Auto-Regressive model , 2015 .

[50]  Bri-Mathias Hodge,et al.  Improved Wind Power Forecasting with ARIMA Models , 2011 .

[51]  H Zareipour,et al.  Wind Power Prediction by a New Forecast Engine Composed of Modified Hybrid Neural Network and Enhanced Particle Swarm Optimization , 2011, IEEE Transactions on Sustainable Energy.