Super-resolution perception for wind power forecasting by enhancing historical data - Liu, Guolong; Zhang, Shuwen; Zhao, Huan; Liu, Jinjie; Liang, Gaoqi; Zhao, Junhua; Sun, Guangzhong (Frontiers in Energy Research, 2022)
As an important part of renewable energy, wind power is crucial to the realization of carbon neutrality. How to accurately predict the wind output so that it can be integrated into the power grid as much as possible to enhance its utilization rate is worth studying. In this paper, a data enhancement method based on Super Resolution Perception (SRP) namely Super Resolution Perception Wind Power Net (SRPWPN) and a SRP-based framework are proposed to assist wind power forecasting. The proposed method uses SRP technology to first complete the historical meteorological and wind power data collected by industrial devices. The proposed method first detects the completeness and correctness of the historical meteorological and wind power data. Then, the detected errors are corrected and missing data is recovered to make the data complete. The frequency of the data is then increased using the proposed method so that the data becomes complete high-frequency data. Based on the enhanced data with more detailed characteristics, more accurate forecasts of wind power can be achieved, thereby improving the utilization rate of wind power. Experiments based on the public dataset are used to demonstrate the effectiveness of the proposed method and framework.