TY  - EJOU
AU  - Zhang, Ming 
AU  - Li, Hongbo 
AU  - Deng, Xing 

TI  - Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting
T2  - Energy Engineering

PY  - 2022
VL  - 119
IS  - 1
SN  - 1546-0118

AB  - 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%.
KW  - Wind power forecasting; correlation analysis; inferential statistics; neural network-related approaches

DO  - 10.32604/EE.2022.017916