Corrected multi-resolution ensemble model for wind power forecasting with real-time decomposition and Bivariate Kernel density estimation

Abstract The power integration is a challenge for the power system because of the fluctuation of the wind power. Wind power forecasting can estimate the future fluctuation of the wind power, and enhance the safety of the power integration. In this study, a corrected multi-resolution forecasting model is proposed to improve current wind power forecasting performance. The proposed model contains three stages, including multi-resolution ensemble, adaptive multiple error corrections and uncertainty estimation. Four real-time wind power data sets are applied to verify the effectiveness of the proposed model. The results are shown as follows: (a) the proposed model is effective for wind power forecasting, the 1-step index of agreement and coverage width-based criterion with 99% confidence level of the proposed model on the dataset #1 are 0.9432 and 0.6951 respectively; (b) the proposed model outperforms the previous models. Through techno-economic analysis, it can be concluded that the proposed model has the potential to be applied to improve the power integration performance.

[1]  Zhipeng Li,et al.  Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network , 2019, Renewable Energy.

[2]  Hui Liu,et al.  Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine , 2019, Energy Conversion and Management.

[3]  Hao Zhou,et al.  A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting , 2019, Neurocomputing.

[4]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[5]  Hui Liu,et al.  Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression , 2019 .

[6]  Bri-Mathias Hodge,et al.  A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization , 2019, Applied Energy.

[7]  Peng Kou,et al.  Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts , 2015 .

[8]  Mohammad Ali Ghorbani,et al.  Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data , 2018 .

[9]  Pradipta Kishore Dash,et al.  A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression , 2019, Renewable Energy.

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

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

[12]  Ying WANG,et al.  Irregular distribution of wind power prediction , 2018, Journal of Modern Power Systems and Clean Energy.

[13]  Qinghua Hu,et al.  Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model , 2017 .

[14]  Yanfei Li,et al.  Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network , 2018 .

[15]  Hui Liu,et al.  Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods , 2019, Energy Conversion and Management.

[16]  Chao Chen,et al.  Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction , 2019, Applied Energy.

[17]  Hui Liu,et al.  An evolution-dependent multi-objective ensemble model of vanishing moment with adversarial auto-encoder for short-term wind speed forecasting in Xinjiang wind farm, China , 2019, Energy Conversion and Management.

[18]  Haiping Wu,et al.  An intelligent hybrid model for air pollutant concentrations forecasting: Case of Beijing in China , 2019, Sustainable Cities and Society.

[19]  Feng Gao,et al.  Stochastic Coordination of Plug-In Electric Vehicles and Wind Turbines in Microgrid: A Model Predictive Control Approach , 2016, IEEE Transactions on Smart Grid.

[20]  José Francisco Aldana Montes,et al.  jMetalSP: A framework for dynamic multi-objective big data optimization , 2017, Appl. Soft Comput..

[21]  Jujie Wang,et al.  Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy , 2018, Applied Energy.

[22]  Bin Wang,et al.  Adjustable Robust Real-Time Power Dispatch With Large-Scale Wind Power Integration , 2015, IEEE Transactions on Sustainable Energy.

[23]  Wei-Min Lin,et al.  Payback period for residential solar water heaters in Taiwan , 2015 .

[24]  A. Bridgwater,et al.  Techno-economic and uncertainty analysis of Biomass to Liquid (BTL) systems for transport fuel production , 2018 .

[25]  R. Weron,et al.  Recent advances in electricity price forecasting: A review of probabilistic forecasting , 2016 .

[26]  Athanasios V. Vasilakos,et al.  Machine learning on big data: Opportunities and challenges , 2017, Neurocomputing.

[27]  Ping Jiang,et al.  Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting , 2019, Appl. Soft Comput..

[28]  Hui Liu,et al.  Data processing strategies in wind energy forecasting models and applications: A comprehensive review , 2019, Applied Energy.

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

[30]  Yalian Yang,et al.  Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization , 2016 .

[31]  C. H. Kim,et al.  Integrated techno-economic analysis under uncertainty of glycerol steam reforming for H2 production at distributed H2 refueling stations , 2019, Energy Conversion and Management.

[32]  Xinsong Niu,et al.  A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting , 2019, Applied Energy.

[33]  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.

[34]  Yang Li,et al.  Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs , 2017, IEEE Power and Energy Magazine.

[35]  Ranjeeta Bisoi,et al.  Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression , 2018, Renewable Energy.

[36]  A. S. Dokuz,et al.  Wind power forecasting based on daily wind speed data using machine learning algorithms , 2019, Energy Conversion and Management.

[37]  Azim Heydari,et al.  A novel composite neural network based method for wind and solar power forecasting in microgrids , 2019, Applied Energy.

[38]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[39]  Qinghua Hu,et al.  Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning , 2018 .

[40]  T. J. Rogers,et al.  Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression , 2020, Renewable Energy.

[41]  H. Zou,et al.  Does inflation cause growth in the reform-era China? Theory and evidence , 2016 .

[42]  Hui Liu,et al.  Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy , 2019, Renewable Energy.

[43]  Yan Li,et al.  Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia , 2018, Adv. Eng. Informatics.