Journal
IET RENEWABLE POWER GENERATION
Volume 12, Issue 12, Pages 1424-1430Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rpg.2018.5203
Keywords
power engineering computing; wind; mean square error methods; load forecasting; wind power plants; backpropagation; neural nets; time series; wind power; power generation scheduling; Levenberg-Marquardt back-propagation NN; correlation coefficient; root mean square error; maximum absolute error; intrinsic mode functions; wind speed forecasting models; historical wind speed; wind speed time series; short-term wind speed forecasting; variational mode decomposition; artificial neural networks model; wind energy; VMD; ANNs model; forecasting methods; forecasting accuracy
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Funding
- National Natural Science Foundation of China [61102124]
- Natural Science Foundation of Liaoning Province [2015020064]
- Educational Commission of Liaoning Province [LQGD2017035]
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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.
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