Ultra-short-term wind energy prediction based on wavelet denoising and multivariate LSTM

Wind energy is a typical representative of environmentally friendly new energy. However, its huge randomness and suddenness have caused many harms and losses to the actual applied wind power. Therefore, predicting wind energy in advance and improving the prediction accuracy have become the top priority. Since the wind energy data is a kind of time series, LSTM model has excellent performance. Most researches are focused on one-dimensional wind energy data. This paper uses a multivariate LSTM model. In addition to weather conditions, the wind energy at the previous moment is included as one of the variables to establish a prediction model. The model built including the wind power at the last moment performed very well. This paper compares it with the prediction model that does not consider the wind power at the previous moment, and verifies its effectiveness.The innovation of this paper is that firstly, wavelet denoising, a signal processing method, is applied to wind energy data processing, and secondly, a multivariate LSTM model is used to forecast wind energy. Considering that wind energy is a time series, the wind energy at the previous moment is also introduced except wind speed and wind direction. Compared with the prediction model that does not include the wind energy at the previous moment, the accuracy is significantly improved.