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Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

Ming Zhang, Hongbo Li, Xing Deng*

School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

* Corresponding Author: Xing Deng. Email: email

Energy Engineering 2022, 119(1), 237-252. https://doi.org/10.32604/EE.2022.017916

Abstract

The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To verify the final performance of the prediction findings, five benchmark methodologies are used: Persistence model, LMNN (Multilayer Perceptron with LM learning methods), NARX (Nonlinear autoregressive exogenous neural network model), LMRNN (RNNs with LM training methods) and LSTM (Long short-term memory neural network). Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model, LMNN, NARX network, and LMRNN, and the 23-steps forecasting accuracy has improved by 19.61%.

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Cite This Article

Zhang, M., Li, H., Deng, X. (2022). Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting. Energy Engineering, 119(1), 237–252.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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