Wind Power Interval Prediction via an Integrated Variational Empirical Decomposition Deep Learning Model

Abstract

As global demand for renewable energy increases, wind energy has become an important source of clean energy. However, due to the instability and unpredictability of wind energy, predicting wind power becomes one of the keys to resolving the instability of wind power. The current point prediction model of wind power output has limitations and randomness in processing information. In order to improve the prediction accuracy and efficiency of wind power, a multi-step interval prediction method (VMD-TCN) is proposed in this article, which uses variational modal decomposition and an improved temporal convolutional network model to predict wind power. Additionally, it introduces attention mechanism, further improving the prediction performance of the model. The method first uses empirical mode decomposition to decompose the wind power generation sequence into six parts and obtains the trend, oscillation and noise components of the output power sequence; then, it optimizes the parameters of the six components, respectively, and uses the interval prediction method combined with the temporal convolutional network to construct a new power prediction model. Experiments show that the proposed method can effectively improve the prediction performance of the power prediction model, and it has strong robustness in interval prediction and high sensitivity to load changes, which can well help power system scheduling and new energy consumption.