NDLI: Performance comparison of ANNs model with VMD for short-term wind speed forecasting

Content Provider	IET Digital Library
Author	Gendeel, Mohammed Yuxian, Zhang Aoqi, Han
Abstract	With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed forecasts, and accurate wind speed forecasts are necessary to schedule power system. In this study, an artificial neural networks (NNs) model with a variational mode decomposition (VMD) for a short-term wind speed forecasting was presented. To reduce the non-stationary of wind speed time series, the historical wind speed was decomposed into different intrinsic mode functions (IMFs) by a VMD. The back-propagation NN with Levenberg–Marquardt was adopted to build sub-models according to the different characteristic of each IMF. The sub-models corresponding to different IMFs were superposed to obtain wind speed-forecasting models. In the experiment, the proposed forecasting model was compared with an NN with wavelet decomposition and empirical mode decomposition. The performance was evaluated based on three metrics, namely maximum absolute error, root mean square error and the correlation coefficient. The comparison results indicate that significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.
Starting Page	1424
Ending Page	1430
Page Count	7
ISSN	17521416
Volume Number	12
e-ISSN	17521424
Issue Number	Issue 12, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/12/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2018.5203
Journal	IET Renewable Power Generation
Publisher Date	2018-07-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ANNs Model Artificial Neural Network Model Backpropagation Correlation Coefficient Forecasting Accuracy Forecasting Method Historical Wind Speed Interpolation And Function Approximation Intrinsic Mode Function Levenberg-Marquardt Back-propagation NN Load Forecasting Maximum Absolute Error Mean Square Error Method Neural Computing Technique Neural Nets Numerical Analysis Numerical Approximation And Analysis Power Engineering Computing Power Generation Scheduling Power System Planning And Layout Probability Theory Root Mean Square Error Short-term Wind Speed Forecasting Statistics Stochastic Linearised SCUC Time Series Variational Mode Decomposition VMD Wind Wind Energy Wind Power Wind Power Plant Wind Speed Forecasting Model Wind Speed Time Series
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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