Prediction of Power Grid Energy Storage Capacity Demand Based on LSTM

With the continuous development of the power system, accurately predicting the power grid energy storage capacity demand is crucial for enhancing the stability and economy of the power system. This paper proposes a prediction method for power grid energy storage capacity demand based on the Long Short-Term Memory (LSTM) network. The LSTM network can effectively handle the long-term dependency problem in time series data and is suitable for the time series prediction of power grid energy storage capacity demand, which is affected by various complex factors. This paper utilizes its unique gating mechanism to process the time series affected by complex factors such as the intermittency of new energy generation and dynamic load changes. By collecting multi-dimensional data of a certain regional power grid over many years, including historical load, new energy generation, meteorology, and holidays, the model is optimized using the Adaptive Moment Estimation (Adam) optimizer and Dropout technology. Experiments show that the LSTM model can effectively improve the prediction accuracy and provide strong support for the planning and construction of power grid energy storage.