Multi-task short-term reactive and active load forecasting method based on attention-LSTM model

With the rapid development of power markets, smart grids and large-scale renewable energy generation, it is crucial to be able to accurately predict both reactive and active power loads. In this paper, we propose a novel multi-task load forecasting method for predicting both active and reactive power simultaneously. The long shortterm memory (LSTM) architecture is employed in the backbone prediction model, supported by an attention mechanism to prevent performance deterioration. Considering the latent dynamic correlations between the reactive and active loads of a substation, multi-task regression based on hard parameter sharing is adopted to treat the forecasting of both types of loads as parallel subtasks. Meanwhile, we design an adaptive combinedtask-wise loss function to optimize the proposed multi-task load forecasting model to avoid biasing of the final model for any subtask. We compare our multi-task attention-LSTM (MTAL) model with other popular singletask load forecasting models and achieve superior accuracy on both subtasks. The results indicate that the proposed method is robust and reliable for practical applications in power systems.

Automated summary

In this paper, a novel multi-task load forecasting method is proposed using LSTM architecture and attention mechanism to predict both active and reactive power loads simultaneously. With multi-task regression based on hard parameter sharing and adaptive combined task-wise loss function, the proposed method optimizes forecasting accuracy for both types of loads. The results demonstrate the robustness and reliability of the proposed multi-task load forecasting model in practical power system applications.