Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration

Abstract Wind power prediction decreases the uncertainty of the entire energy system, which is essential for balancing energy supply and demand. In order to improve the prediction accuracy, a short-term wind power prediction method based on data cleaning and feature reconfiguration is proposed. A large number of historical samples consisting of wind direction, wind speed, and wind power are mapped into a multidimensional sample space, and the distribution of wind data in different dimensions are analyzed in depth. By calculating the local density of each sample, outliers are effectively detected. The features of wind are reconfigured into a global information map combined with the time series information, which reflects the variation of the wind process in the short term. The features of the original data are greatly enriched, providing a high-quality training set for the prediction model. A redesigned convolutional neural network was used to predict short-term wind power, and the proposed methods were trained and tested based on a dataset of a real wind farm in China. Data cleaning and feature reconfiguration reduce the average single-point error by 1.38% and 2.56%, respectively, while the combined method reduced it by 6.24%. Plenty of experimental results show that the proposed methods achieve good performance and effectively improve the accuracy of short-term wind power prediction.

[1]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[2]  Karsten Beckmann,et al.  Design Considerations for Memristive Crossbar Physical Unclonable Functions , 2018, ACM J. Emerg. Technol. Comput. Syst..

[3]  Jiani Heng,et al.  A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting , 2019, Applied Energy.

[4]  Changliang Liu,et al.  Abnormal Detection of Wind Turbine Operating Conditions Based on State Curves , 2019, Journal of Energy Engineering.

[5]  Wenhu Tang,et al.  Wind power forecasting using attention-based gated recurrent unit network , 2020 .

[6]  Hao Yu,et al.  A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks , 2017, ACM J. Emerg. Technol. Comput. Syst..

[7]  Bo Wang,et al.  A novel clustering algorithm based on mathematical morphology for wind power generation prediction , 2019 .

[8]  Lei Zhang,et al.  Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal , 2019, Applied Energy.

[9]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[10]  张帆,et al.  Data Pre-processing Method and its Evaluation Strategy of SCADA Data from Wind Farm , 2016 .

[11]  Hui Liu,et al.  Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network , 2018 .

[12]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[13]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[14]  Jordan Nielson,et al.  Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines , 2019, Wind Engineering.

[15]  Hongxing Yang,et al.  Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model , 2020 .

[16]  Harish Garg,et al.  DISTANCE AND SIMILARITY MEASURES FOR DUAL HESITANT FUZZY SOFT SETS AND THEIR APPLICATIONS IN MULTICRITERIA DECISION MAKING PROBLEM , 2017 .

[17]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[18]  Y. Hirano,et al.  1.55-μm high-peak, high-average-power laser amplifier using an Er,Yb:glass planar waveguide for wind sensing coherent Doppler lidar. , 2019, Optics express.

[19]  P. Pinson,et al.  Adaptive robust polynomial regression for power curve modeling with application to wind power forecasting , 2016 .

[20]  Harish Garg,et al.  DISTANCE MEASURES BETWEEN THE COMPLEX INTUITIONISTIC FUZZY SETS AND THEIR APPLICATIONS TO THE DECISION-MAKING PROCESS , 2017 .

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Yu Jiang,et al.  Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations , 2018 .

[23]  Severino F. Galán,et al.  Comparative evaluation of region query strategies for DBSCAN clustering , 2019, Inf. Sci..

[24]  Thomas Huld,et al.  Simulating European wind power generation applying statistical downscaling to reanalysis data , 2017 .

[25]  Asifullah Khan,et al.  A survey of the recent architectures of deep convolutional neural networks , 2019, Artificial Intelligence Review.

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

[27]  David Watson,et al.  Automatic Generation Control Using an Energy Storage System in a Wind Park , 2018, IEEE Transactions on Power Systems.

[28]  Q. Tu,et al.  Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution , 2019, Applied Energy.

[29]  Daniel L. Marino,et al.  Building energy load forecasting using Deep Neural Networks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[30]  Cyrus Shahabi,et al.  Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ICLR.

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

[32]  Bri-Mathias Hodge,et al.  The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales , 2016 .

[33]  Volker Coors,et al.  One dimensional convolutional neural network architectures for wind prediction , 2019, Energy Conversion and Management.

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

[35]  Xiaojun Shen,et al.  A Combined Algorithm for Cleaning Abnormal Data of Wind Turbine Power Curve Based on Change Point Grouping Algorithm and Quartile Algorithm , 2019, IEEE Transactions on Sustainable Energy.

[36]  Ying-Yi Hong,et al.  A hybrid deep learning-based neural network for 24-h ahead wind power forecasting , 2019, Applied Energy.

[37]  H. Peng,et al.  Assessment and optimization of the power performance of twin vertical axis wind turbines via numerical simulations , 2020 .