Application of LSTM Network Combining Global and Local Learning in Power Load Forecasting

Power load forecasting is essential for maintaining the equilibrium between power supply and demand, optimizing resource distribution, enhancing operational efficiency, and minimizing operational costs. The acceleration of industrialization and urbanization, along with the extensive implementation of renewable energy, dramatically heightens the uncertainty and complexity of power load, imposing greater demands on the accuracy and dependability of power load forecasting. Currently, load forecasting research predominantly centers on predicting individual load series, meaning each model is exclusively relevant to a particular load series. This necessitates training various models to forecast varied loads, augmenting the complexity and time expenditure of model training. Moreover, the predictive accuracy of the conventional model will markedly diminish as the user's electricity usage patterns evolve. This research proposes a novel forecasting method that integrates the concepts of a global model with model localization to address the issues mentioned above. A global model is initially trained to manage various load sequences to acquire preliminary forecasting capacity. The prediction accuracy is enhanced by fine-tuning the parameters of the global model to create localized models that conform to the specific characteristics of the various load sequences. The experimental results indicate that the proposed model has markedly enhanced prediction accuracy relative to the standard approach.