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 stations. However, as many stations comprise multiple different output characteristics and complementary coupling, potential these cannot be simply based on superposition outputs, this method lacks reasonable assessment results. This study proposes combining Grey relational analysis (GRA), artificial neural network (ANN), XGBoost algorithm First, GRA ANN are used between meteorological factors. Second, factors high correlation existing historical data predict future outputs via XGBoost. Finally, according predicted output, an that includes available capacity coefficient (AOC) other evaluation indicators proposed. The case studies in research prove effectiveness applicability proposed method.