A development potential assessment method for clean energy stations

Clean energy is expected to enter a new stage of large-scale development along with the growing demand for building regional clean energy stations. However, as many regional clean energy stations comprise multiple stations with different output characteristics and complementary coupling, the development potential of these stations cannot be simply based on the superposition of outputs, as this method lacks reasonable assessment results. This study proposes a method of combining Grey relational analysis (GRA), artificial neural network (ANN), and XGBoost algorithm for the potential assessment of clean energy stations. First, GRA and ANN are used for the relational analysis between the output of clean energy stations and meteorological factors. Second, the meteorological factors with high correlation and the existing historical data are used to predict the future outputs of new clean energy stations via XGBoost. Finally, according to the predicted output, an assessment method that includes available capacity coefficient (AOC) and other evaluation indicators is proposed. The case studies in this research prove the effectiveness and applicability of the proposed method.

Automated summary

This study proposes a method of combining Grey relational analysis (GRA), artificial neural network (ANN), and XGBoost algorithm for the potential assessment of clean energy stations. The method involves analyzing the relationship between the output of clean energy stations and meteorological factors using GRA and ANN. It also uses historical data and high correlated meteorological factors to predict the future outputs of new clean energy stations via XGBoost. The proposed method includes an assessment method based on predicted output and evaluation indicators such as available capacity coefficient (AOC).