Abstract
The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.
Highlights
With energy shortage and environmental pollution’s further deterioration, the development and utilization of renewable energy is receiving more and more attention from the whole world
Based on the analysis above, this study introduces a novel combined model to improve the accuracy of short-term wind power prediction
The prediction results obtained by the not consider meteorological data (NMD) combined model can further illustrate that the meteorological data has not been considered, the combined model based on three prediction models has significantly improved the accuracy of wind power prediction
Summary
With energy shortage and environmental pollution’s further deterioration, the development and utilization of renewable energy is receiving more and more attention from the whole world. These two techniques have improved the forecasting performance to a certain extent, they still have some disadvantages, such as the mode mixing problem in EMD and the residual noise in EEMD [33] To overcome these defects of EMD and EEMD, a novel technique called ESMD, proposed by Wang et al [34], is employed for reducing the noise and uncertainty of wind speed and wind power series. Based on the analysis above, this study introduces a novel combined model to improve the accuracy of short-term wind power prediction It combines meteorological data (humidity, pressure, temperature, wind direction, and wind speed), the signal decomposition technique, SampEn theory, and several forecasting algorithms, namely the Bat-BP model, Adaboost-ENN model, and ENN. It successfully exploits the advantages of each prediction model for further improvement.
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