Research on a short-term forecast of industry load based on PSO-LSTM

Abstract Load prediction is crucial to maintaining a stable power grid and encouraging the use of innovative energy types like scenescape energy. The industrial load of a region accounts for a significant proportion of the total load, but the industrial load is difficult to predict due to its large fluctuations and instability. This study proposes a short-term power load prediction model, merging the Particle Swarm Optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network. The PSO algorithm fine-tunes the LSTM network’s hyperparameters, which are then implemented on the LSTM layer. The trained model, using real-world datasets, is then tested for accuracy. Our model’s effectiveness is backed by practical examples.