An Ultra-Short-Term Wind Power Forecasting Model Based on EMD-EncoderForest-TCN

Abstract

Accurate wind power prediction helps to stabilize the operation of the power system, improve the utilization rate of renewable energy, reduce dependence on traditional energy, and achieve sustainable energy development. An ultra short-term wind power prediction method integrating EMD-EncoderForest-TCN is proposed to address the difficulty of predicting wind power due to frequent changes in wind speed. Firstly, the time-series input data of the model is decomposed into high-frequency and low-frequency components using Empirical Mode Decomposition. Then, based on the EncoderForest model and TCN model, differential information extraction is performed on the low-frequency and high-frequency components. The EncoderForest model regularizes low-frequency information and captures trend patterns in the data. The TCN model models the high-frequency components of time series to capture complex patterns and structures in wind power. Finally, based on convolutional neural networks, the output results of each part are calculated to achieve accurate prediction of wind power. Based on the operational data of an actual wind farm, conduct a case study analysis. The results show that the proposed model can achieve accurate prediction of short-term wind power, with a prediction accuracy improvement of 2.57%.

Keywords

• Wind power
• empirical mode decomposition
• encoderforest
• temporal convolutional network
• ultra short term wind power prediction