KEYWORDS: Solar energy, Photovoltaics, Data modeling, Education and training, Data conversion, Statistical modeling, Performance modeling, Meteorology, Decision trees, Fourier transforms
With the increasingly prominent problems of energy carbon emissions, environmental pollution, and climate change, China has further accelerated the construction of new energy sources to deal with a series of ecological problems. China's new energy structure mainly relies on photovoltaic and wind power generation. In addition, wind and solar power generation also has problems such as instability and strong randomness, so existing big data and artificial intelligence can be used to solve this problem and improve wind and solar forecasting. In this paper, a double-loop distributed gradient enhancement algorithm is used to improve the power prediction accuracy of photovoltaic power plants, which effectively solves the shortcomings of slow speed and poor prediction stability in photovoltaic power prediction, and ensures the power dispatching problem based on new power systems.
KEYWORDS: Wind energy, Education and training, Decision trees, Data modeling, Wind speed, Meteorology, Machine learning, Data conversion, Atmospheric modeling, Industry
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.
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