@Article{EE.2022.017916,
AUTHOR = {Ming Zhang, Hongbo Li, Xing Deng},
TITLE = {Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting},
JOURNAL = {Energy Engineering},
VOLUME = {119},
YEAR = {2022},
NUMBER = {1},
PAGES = {237--252},
URL = {http://www.techscience.com/energy/v119n1/45669},
ISSN = {1546-0118},
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%.},
DOI = {10.32604/EE.2022.017916}
}