More accurate prediction of renewable energy electricity demand is an important indicator for predicting the direction and effectiveness of future energy structure adjustments.The ISSA-LSTM com-bined forecasting model was used to predict the electricity demand for renewable energy in China from 2023 to 2030.Firstly,the Sparrow Search Algorithm(SSA)was improved using Circle Chaos Mapping to enhance search capability and population diversity.Then the long Short Term Memory Neural Network(LSTM)was introduced to effectively capture the random fluctuations and temporal characteristics of renewable energy electricity demand.Finally,the ISSA-LSTM model was used to predict long-term re-newable energy electricity demand,to validate the test set data,and to compare it with other traditional models.The results show that the ISSA-LSTM model can meet the accuracy requirements for predicting renewable energy electricity demand;In the future from 2023 to 2030,the demand for renewable energy electricity will remain stable and not fluctuate significantly,reaching one-third of the national electricity consumption;The use of Circle chaotic mapping to improve the strategy can effectively enhance the op-timization ability of SSA.Compared with PSO algorithm,SSA algorithm has better ability to find the op-timal solution of LSTM hyperparameters,and the ISSA-LSTM model has higher accuracy in predicting renewable energy electricity demand.
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