Wind power prediction method based on multi-loop improved gradient boosting decision tree

Abstract

With the increase in energy demand, carbon emissions, environmental pollution, climate change and other issues have become increasingly prominent, China has accelerated the construction of new energy sources. Especially in the field of wind power generation, it is the most potential type of large-scale development of non-hydroelectric renewable energy. Due to the volatility, intermittency and low energy density of wind power, the power of wind power also fluctuates. However, with the early digital transformation of my country's energy industry, a large number of meteorological environments and equipment measurement points have been accumulated in wind power production sites. Power generation related data, using artificial intelligence, deep learning and other technologies can effectively predict the power generation of the station with high precision. A model algorithm of multi-loop gradient boosting decision tree is used in this paper, considering the stationarity test of time series and wind power fluctuation attribute, the accuracy of wind power prediction is effectively improved. Help the power dispatching department to pre-arrange dispatching plans according to wind power changes. Ensure the smooth and safe operation of the power grid.