Renewable Energy Quantity Forecasting Method Based on ResLSTM

Under the background of domestic power purchasing agent, whether the renewable energy can be accurately predicted in the region directly affects the income of power generation and transmission enterprises. It is urgent to strengthen the monthly electricity forecasting method of renewable power oriented to engineering practice. In this paper, an improved ResLSTM model is proposed, which makes use of the advantages of different network learning methods in wind, light and water power generation prediction to form a comprehensive prediction model under multiple external environments. The prediction model can effectively simplify the classification of sub-item power sources and carry out the complicated operation of prediction by different methods under the condition of simplification and application, and keep the prediction accuracy. The method presented in this paper is verified by the actual measurement in the region, and good practical effect is achieved in the region.