Load Forecasting Method Based on QPSO-LSTM

With the continuous expansion of the power system scale and the increasing growth of electricity demand, accurate load forecasting is of great significance for the safe, stable operation and economic dispatch of the power system. Traditional load forecasting methods have certain limitations in handling complex load data and are difficult to meet actual needs. This paper proposes a load forecasting method based on the Quantum Particle Swarm Optimization(QPSO) algorithm and the Long Short-Term Memory(LSTM) network. Firstly, the load data is pre-processed, including normalization and dataset division. Then, the LSTM model is used to learn the time-series characteristics of the load data. At the same time, the QPSO algorithm is adopted to optimize the hyperparameters of the LSTM model to improve the prediction performance of the model. Through practical example analysis, it is shown that this method can effectively improve the accuracy and stability of load forecasting. Compared with traditional methods, it performs better in handling complex load data and can provide a more reliable decision-making basis for the planning and operation of the power system.