Deep Learning Based Visualized Wind Speed Matrix Forecasting Model for Wind Power Forecasting

With the increasing proportion of wind power generation in the power grid, the randomness and volatility of wind power generation have a more and more serious impact on the power grid operation. Therefore, accurate wind power forecasting is an essential guarantee for the power system's safe and stable operation. Wind power is mainly affected by wind speed, so precise forecasting of wind speed is imperative. Given the insufficient use of information by current wind speed forecasting methods, this paper makes full use of information in two dimensions of time and space. It proposes a method of deep learning based visualized wind speed matrix forecasting model for wind power forecasting. First, the wind speed matrix is formed by extracting the wind speed data under different coordinates. Secondly, the wind speed matrix is converted into a gray image of wind speed by a visualization method. The mapping relationship between the pixels in the gray image and the actual wind speed is established. The convolutional neural network (CNN) algorithm inputs a series of wind speed images and makes forecasting to obtain the forecasted wind speed images and uses the mapping relationship between pixels and wind speed to get the forecasted wind speed data. Finally, the results are compared with the Long short-term memory (LSTM) method. The simulation results show that the method proposed in this paper has higher forecasting accuracy and better robustness in the period when wind speed fluctuates wildly.