Wind power generation forecasting system based on multi-model intelligent fusion strategy and probabilistic forecasting technology.

Abstract

Due to the excessive consumption of fossil energy, mankind is facing a series of severe challenges such as resource depletion and environmental deterioration, which makes the development, utilization and promotion of clean energy become an inevitable trend in the world. Wind energy as a typical renewable energy, with its clean, environmental protection characteristics in the field of new energy rapid development. However, due to the intermittent and instantaneous fluctuations of wind power, large-scale wind power grid integration and stable operation of power systems face difficult tests. Therefore, accurate and efficient wind prediction is very important for the stability control and integrated scheduling of wind turbines. Based on data from different wind turbines at the Penmanshiel wind farm on the east coast of Scotland, this paper makes deterministic predictions and uncertainty analyses for the next 24, 48 and 72 hours and proposes an integrated wind power system. In data preprocessing phase, the adaptive decomposition reconstruction strategy combined with fuzzy theory, effectively reduce the noise and fluctuations on the result of the experiment data. On this basis, the optimization algorithm is integrated to carry out parameter fine-tuning and structure optimization. Finally, with the aid of quantile regression and kernel density estimation, a scientific, accurate and stable forecasting system is constructed. Compared with the traditional single model forecast, the system not only quantifies the uncertainty of wind forecast, but also improves the forecast accuracy.