Generation Prediction for Hydropower Considering the Coupling Effect of Adjacent Plants

In recent years, due to the need for economic development and the launch of the double carbon target, a large amount of hydropower has been put into operation, posing a threat to the safety of the power grid and causing abandonment problems in some areas during the flood season. Therefore, fully grasping hydropower generation forecasting technology is an important way to ensure the safe and stable operation of the power grid and reduce the problem of water abandonment. For the existing hydropower generation forecasting technology lacks the analysis of the impact of upstream and downstream neighboring hydropower. A generation forecasting method that considers the coupling effect of adjacent hydropower is proposed. The method considers the hydropower runoff topology relationship and uses the relevant generation data of adjacent hydropower as features to model the generation of the target hydropower. The hydropower generation power is affected by multidimensional meteorological factors such as precipitation, runoff and temperature, etc. The EEMD-LSTM prediction model is constructed by considering the characteristic relationship of hydropower power-meteorological factors and the time-series characteristics of hydropower power, and the model is trained by using the historical power data and meteorological data of neighboring power stations and the target power station. Finally, the model is analyzed based on the actual data of a basin hydropower plant. The results show that the proposed model can effectively improve the prediction accuracy of hydropower generation power.